Olivier Chapuis

Raphaël James

Marzieh Rafiei

Anastasia Bezerianos

Comparing Physical and VR-emulated Ultra-walls in a Classification Task

Keywords: Human-centered computing → Graphical user interfaces; Virtual reality Head mounted display, Wall-sized display, Pixel density, Pick-and-Drop, Vitual navigation Casque de réalité virtuelle, mur d'écrans, densité des pixels, Pick-and-Drop, Navigation virtuelle

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INTRODUCTION

Ultra-high resolution wall-sized displays (ultra-walls) are large wall displays with a pixel density similar to that of the screen of a workstation (typically 100dpi). They allow to display a considerable amount of pixels and are appropriate platforms for manipulating a large amount of data [START_REF] Ball | Move to improve: promoting physical navigation to increase user performance with large displays[END_REF][START_REF] Mikkel | Interactive Visualizations on Large and Small Displays: The Interrelation of Display Size, Information Space, and Scale[END_REF][START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF][START_REF] Rajabiyazdi | Understanding Researchers' Use of a Large, High-Resolution Display Across Disciplines[END_REF][START_REF] Reda | Effects of Display Size and Resolution on User Behavior and Insight Acquisition in Visual Exploration[END_REF]. However, ultra-walls are expensive, need large rooms, and are complex to set up and maintain. For instance, the cost of the wall used in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] to compare a desktop and an ultra-wall, and the one used in our second study, can be estimated to at least 100kC. In addition, ultra-walls need large rooms, and given the number of pixels to be rendered, they should be driven by a cluster of high-end workstations (at least for now). For these reasons, these platforms, although powerful, can be very power-consuming and complex to setup, maintain and program for.

In comparison, Virtual Reality (VR) head-mounted displays (HMDs) are not too expensive, do not necessarily need large rooms, are easy to maintain, and benefit from various available development environments, such as Unity3D.

Moreover, work in the recent Immersive Analytics trend [START_REF] Chandler | Immersive Analytics[END_REF][START_REF] Ribarsky | Visualization and Analysis Using Virtual Reality[END_REF] suggests using HMDs for data analytics [START_REF] Cordeil | Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display[END_REF][START_REF] Kraus | The Impact of Immersion on Cluster Identification Tasks[END_REF][START_REF] Wagner Filho | Immersive Visualization of Abstract Information: An Evaluation on Dimensionally-Reduced Data Scatterplots[END_REF].

Although 3D visualization for abstract data has some drawbacks [START_REF] Elmqvist | 3D Visualization for Nonspatial Data: Guidelines and Challenges[END_REF][START_REF] Munzner | Visualization Analysis and Design[END_REF], it is nevertheless possible to display them on 2D surfaces rendered in HMDs.

Thus, a natural research question is to compare the performance of an HMD with an ultra-wall when manipulating a large amount of data. This article explores this question, examining in particular whether it is possible to emulate an ultra-wall in an HMD (as is done in Figure 1) with respect to its ability to show large amounts of information.

The size and the pixel density of an ultra-wall determine the quantity of information that can be displayed. Both characteristics are fundamental in the study of ultra-walls [START_REF] Endert | Visual encodings that support physical navigation on large displays[END_REF][START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF][START_REF] Yost | Beyond visual acuity: the perceptual scalability of information visualizations for large displays[END_REF]. The big size of an ultra-wall allows for displaying a large number of dataset elements concurrently, as its high pixel density allows to display these elements (e.g., images, plots, or text descriptions) in high enough resolution to be easily distinguishable and readable.

As HMDs could emulate a wall display of arbitrary size, in this paper, we start by analyzing, in a theoretical way, the pixel density of a virtual wall rendered in HMD. We take as examples the HTC Vive Pro 1 & 2 and the Varjo XR-1 & XR-3. These HMDs are currently the highest-resolution commercially available HMDs. As we will see, headsets' pixel density is sub-optimal and does not allow, a priori, to emulate the pixel density of an ultra-wall.

Then, to empirically validate the above computational approach, we run two user studies. For this purpose, we used the classification task that has been proposed by Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], in their case to compare an ultra-wall and a desktop. This task has been used in several other works to compare interaction techniques or paradigms [START_REF] Avellino | CamRay: Camera Arrays Support Remote Collaboration on Wall-Sized Displays[END_REF][START_REF] Mikkel | Is Moving Improving? Some Effects of Locomotion in Wall-Display Interaction[END_REF][START_REF] Liu | Shared Interaction on a Wall-Sized Display in a Data Manipulation Task[END_REF][START_REF] Normand | Enlarging a Smartphone with AR to Create a Handheld VESAD (Virtually Extended Screen-Aligned Display)[END_REF]. The advantage of this task is that it involves both interaction (pick-and-drop) and navigation (virtual or physical pan & zoom). More importantly, the task operationalizes information density, and thus the quantity of information the device can render. Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] operationalizes this information density by simply considering classes represented by letters of different sizes. Large letters operationalize low information density (see Figure 1), and small letters operationalize high information density.

In our first study, we consider a special emulation of a wall by considering a scaled virtual wall to make the task manageable. We also consider a simulation of an HMD that features a pixel density matching human visual capacity (60 pixels per degree). We use virtual navigation techniques (Teleport and Drag) to deal with our huge scaled virtual wall and situations where users cannot physically travel (no space available). In the second study, we compare an actual physical wall, a Vive Pro, and a Varjo XR-1, and a too-optimistic simulation of a "perfect-resolution" HMD under physical navigation (users can walk in the room). In both studies, we used the exact same task as Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], allowing us to compare the results of our studies and those of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF].

Combined, our results suggest that the pixel density of state-of-the-art HMDs does not allow to reach the performance of an ultra-wall but that this might be possible in the future with HMDs that reach human visual capacity (e.g., two displays at 2600 dpi with a resolution of 5600×5600 pixels). However, our results also suggest that even with such HMDs, users would most likely prefer to use an ultra-wall.

RELATED WORK

To better understand the differences between HMDs and wall displays, our related work focuses on the effects of the size and resolution of displays and previous findings of comparisons between HMDs and large displays.

Screen Size and Resolution

There is a long history in HCI about comparing screens of different sizes and resolutions. Pioneer work mainly compares standard monitors with larger screens (e.g., multi-monitor setting) in high level "desktop tasks" [START_REF] Bi | Comparing Usage of a Large High-Resolution Display to Single or Dual Desktop Displays for Daily Work[END_REF][START_REF] Czerwinski | Toward characterizing the productivity benefits of very large displays[END_REF][START_REF] Dugald | Display Space Usage and Window Management Operation Comparisons between Single Monitor and Multiple Monitor Users[END_REF][START_REF] Robertson | The Large-Display User Experience[END_REF]. Most of these results suggest that users are more efficient with larger screens. The above works consider users seated in front of the display, and thus physically static (besides eyes and head movement). However, with very large screens of high resolution, like ultra-walls, it is not possible for the users to see (distinguish) all the pixels at a fixed position, and thus users should physically travel the wall room to take advantage of both the size and the high resolution, or should use virtual navigation (e.g., pan & zoom) to bring information closer to them. We consider both such navigation in our work.

Interestingly, it has been shown that, for an ultra-wall (2.7m wide, 96 dpi), physical navigation is more efficient than virtual navigation for simple search and pattern finding tasks [START_REF] Ball | Move to improve: promoting physical navigation to increase user performance with large displays[END_REF]. Several subsequent works studied the cause of this effect. Ball and North [START_REF] Ball | The Effects of Peripheral Vision and Physical Navigation on Large Scale Visualization[END_REF] suggests that the field of view is not a key factor, Ragan et al. [START_REF] Ragan | How Spatial Layout, Interactivity, and Persistent Visibility Affect Learning with Large Displays[END_REF] suggests that the constant positioning of information is important, Jansen et al. [START_REF] Jansen | Effects of Locomotion and Visual Overview on Spatial Memory When Interacting with Wall Displays[END_REF] studies spatial memory, and Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] advocates that the main cause is the power of head movements to reach information (i.e., immersion when close to the wall). Moreover, Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] insist that such results depend on the "density of information" and the full use of the high resolution.

HMD versus Other Displays

There are a lot of works that compare HMDs with desktops and CAVE style environments. Santos et al. [START_REF] Sousa | Headmounted display versus desktop for 3D navigation in virtual reality: a user study[END_REF] provides a good overview of such works done before 2009. Most of this work concerns tasks in a 3D virtual world (e.g., navigation in a maze or buildings, volume, visualization, etc). Results are mixed regarding performance, but HMDs are often preferred and some results suggest that users create a better mental model of the environment with HMDs. Subsequent works are in the same lines: 3D tasks where resolution is secondary, have very few results on performance differences, but find some perceptual advantage for HMDs. For instance, Lhemedu-Steinke et al. [START_REF] Lhemedu-Steinke | Comparing VR Display with Conventional Displays for User Evaluation Experiences[END_REF] suggests a better concentration, involvement and enjoyment with a HMD than with a large display. Also, [START_REF] Daniel Thalmann | An Evaluation of Spatial Presence, Social Presence, and Interactions with Various 3D Displays[END_REF] suggests a better perceived spatial presence of the virtual environment with an HMD than with a desktop. On the other hand, some works report cybersickness issues with HMDs that do not occur with standard displays [START_REF] Brooks | Comparison of a head-mounted display and flat screen display during a micro-UAV target detection task[END_REF][START_REF] Srivastava | Desktop VR is Better Than Nonambulatory HMD VR for Spatial Learning[END_REF].

HMD in Immersive Analytics

The interest around immersive visual analytics [START_REF] Chandler | Immersive Analytics[END_REF][START_REF] Ribarsky | Visualization and Analysis Using Virtual Reality[END_REF] in the visualization and HCI community has increased in the recent years. In particular, HMDs have become very popular for visualizing abstract data with no obvious 3D representation.

Although the use of 3D in this context is disputable [START_REF] Elmqvist | 3D Visualization for Nonspatial Data: Guidelines and Challenges[END_REF][START_REF] Munzner | Visualization Analysis and Design[END_REF], some works have been conducted to take advantage of the immersion and the 3rd dimension to visualize multivariate data [START_REF] Batch | There Is No Spoon: Evaluating Performance, Space Use, and Presence with Expert Domain Users in Immersive Analytics[END_REF][START_REF] Cordeil | ImAxes: Immersive Axes As Embodied Affordances for Interactive Multivariate Data Visualisation[END_REF], scatterplots [START_REF] Kraus | The Impact of Immersion on Cluster Identification Tasks[END_REF][START_REF] Prouzeau | Scaptics and Highlight-Planes: Immersive Interaction Techniques for Finding Occluded Features in 3D Scatterplots[END_REF][START_REF] Wagner Filho | Immersive Visualization of Abstract Information: An Evaluation on Dimensionally-Reduced Data Scatterplots[END_REF], networks [START_REF] Cordeil | Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display[END_REF][START_REF] Ware | Visualizing Graphs in Three Dimensions[END_REF],

small multiples [START_REF] Liu | Design and Evaluation of Interactive Small Multiples Data Visualisation in Immersive Spaces[END_REF], etc. Surprisingly, there are very few works that compare HMD with other types of displays in the context of immersive analytics. Cordeil et al. [START_REF] Cordeil | Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display[END_REF] compares an HMD and a CAVE style 9 m × 1.7 m horseshoe shaped wall display at 34 dpi with stereo 3D rendering for collaborative analysis of connectivity of simple networks. Results suggest that the HMD is more efficient than the CAVE, but collaboration is out of the scope of this report. To evaluate the possible advantage of immersive space-time cube geovisualization, Wagner Filho et al. [START_REF] Wagner Filho | Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration[END_REF] compares an HMD and a desktop, but there is no decisive results in term of performance.

Emulating Walls in VR HMDs

Recent work uses virtual reality HMDs to simulate various types of "physical" environments, in particular walls, to evaluate them without the need to build or deploy the full physical environment. A pioneer work in this area is the work of Mäkelä et al. [START_REF] Mäkelä | Virtual Field Studies: Conducting Studies on Public Displays in Virtual Reality[END_REF] where they suggest conducting field studies on public displays in virtual reality. They observed similar phenomena in real-world and virtual public display setups, such as engagement with the displays. Similar types of work have been conducted to emulate Augmented Reality (AR) [START_REF] Gushima | Virtual Fieldwork: Designing Augmented Reality Applications Using Virtual Reality Worlds[END_REF], real-world authentication prototypes [START_REF] Mathis | Stay Home! Conducting Remote Usability Evaluations of Novel Real-World Authentication Systems Using Virtual Reality[END_REF], cross-reality systems [START_REF] Gruenefeld | VRception: Rapid Prototyping of Cross-Reality Systems in Virtual Reality[END_REF], etc.

However, to our knowledge, the only work that focuses on emulating a wall display is the work Mäkelä et al. [START_REF] Mäkelä | Virtual Field Studies: Conducting Studies on Public Displays in Virtual Reality[END_REF].

Moreover, our goal is different, Mäkelä et al. [START_REF] Mäkelä | Virtual Field Studies: Conducting Studies on Public Displays in Virtual Reality[END_REF] focus on field studies and high-level user engagement, as we want to compare an ultra-wall and HMDs with a focus on their resolution.

BACKGROUND AND GEOMETRICAL MODELS OF VISION

We describe next the well-known geometrical model of vision and through it we examine the possibility of emulating a wall display in an HMD in terms of pixel density. The angular size of an object is the angle a viewed object creates on the human retina. The relationship between the size of a circle of diameter w seen on a screen positioned at distance d from the eye, and it's angular size µ in the human "cyclopean" eye, is given by w 2). A human with normal vision can distinguish an object that is not smaller than an angular size of 1 ′ , i.e., one minute of arc = 1 60 • . For example if the circle mentioned above had an angular size of 1 ′ then the viewer would be able to tell it apart from other similar circles placed side-by-side. This means that if this circle was a screen's pixel, the human vision's optimal resolution would be about 60 pixels per degree (ppd). From this, we can determine the smallest size of such a circle that can be distinguished by the human eye, by setting the angular size to 1 ′ , getting Our Wall (8K screens) 280 0.0900 mm 30.9 cm Fig. 3. Different devices: resolution, corresponding pixel size and optimal viewing distance. A closer distance will result in pixelization; a farther distance will result in details not being perceived. Fig. 4. The wall that we used in the second study.

Principle of the monocular eye for a physical screen

= 2 • d • tan µ 2 (Figure
w min = 2 • d • tan 1 2 ′ ∼ 0.00029 • d.
Conversely, we can compute the "ideal" visual distance from a computer screen given the size p of its pixels, i.e., the distance where the pixel size is equal to the size of a visual pixel, by just inverting the previous equation

d ideal = p 2•tan 1 2
′ . Within this distance, we have a pixelization effect, i.e., users can see the pixels, and beyond it, users might loose information rendered on the screen.

Figure 3 provides examples for standard devices, from a smartwatch to the ultra-wall that we used in the second study (Figure 4). We can observe that, current screen technology for smartwatches, smartphones, laptops, and ultra-walls has reached human visual capacity and beyond.

Principle of the monocular eye for a VR headset

We now turn our attention to HMDs. One particularity of HMDs is that the screens are (and must be) very close to the eyes. They are so close to the eyes that the human eyes cannot accommodate an image rendered on the screen. Thus, common VR HMDs use convex lenses to create a virtual image of the screens at a far distance from the eyes (e.g., 1.5 m), avoiding any need for the eyes to perform an accommodation.

In this context the "encadré" (frame) below explains how to compute (from the characteristics of an HMD) the angular size of a pixel of the virtual image in the users' eyes, the ppd of the HMD, and the ideal size of a pixel on the display, i.e., the size so that the pixel in the virtual image has an angular size of 1 arc minute.

Table 1 provides examples of these values for different headsets. Note that AR headsets such as Hololenses use technology other than convex lenses, but the optic system of such headsets has the same goal: to build, using the projected images on the glasses, a virtual image at a large distance from the eyes. Unfortunately, this table shows our lack of information, because manufacturers do not disclose technical characteristics of their HMDs such as focal distance and the distance from the lens to the screens (we provide our own estimations for the Vive Pro).

However, we can observe that current VR headsets are far from being optimal regarding human visual capacity in their full field of view. Indeed, the bests AR headsets reach 30 ppd (Varjo XR-3, Vive Pro 2), half of what would be needed. The Varjo's use an interesting technology using an additional 1920×1920 pixels display (one per eye and a semi-transparent mirror system) that is "projected" and concentrated in the center of the field of view (we call this display a "focus display", FD in Table 1). Varjo claims that this technology allows reaching the optimal resolution of 60 ppd, and even more, on a part of the field of view (e.g., 27 × 27 degree).

However, covering the entire field of view with a resolution of 60 ppd would require screens with a resolution of about 5600×5600 pixels (more or less, depending on the field of view) or focus displays that can move to follow the users' gaze (and in particular eyes saccades, which seems unlikely, see Kim et al. [START_REF] Kim | Foveated AR: Dynamically-Foveated Augmented Reality Display[END_REF], however).

Let us consider an HMD using convex lenses with focal distance f (and width and height w l and h l ) located between the eyes and the screen (of width and height w s and h s ), at a distance d l from the screen (as seen in Figure 5). As stated by Wetzstein et al. [START_REF] Wetzstein | Build Your Own VR System: An Introduction to VR Displays and Cameras for Hobbyists and Educators[END_REF] during their SIGGRAPH class, the Gaussian thin lens formulas tell us that the distance from the lens to the virtual image equals to:

(1)

d v = | 1 1 f -1 d l |
with d l the distance from the lens to the screen, and f the focal distance of the lens (Figure 5). From this, we can infer that the distance D from the eye to the virtual image is:

(2) Moreover, the size in the virtual image of an object in the display is magnified by the following factor:

D = | 1 1 f -1 d l + d e |
(3)

M = f f -d l
If p is the pixel size of the screen, the size of this pixel in the virtual image is of M • p and its angular size is of:

(4) A = 2 • arctan M•p 2•D .
With this same formula, we can compute the (horizontal or vertical) field of view for one eye by taking for p the (horizontal or vertical) display size. Conversely, we can compute the ideal size of the pixel on the display, i.e., the size so that the pixel in the virtual image has an angular size of 1 arc minute:

(5) 1. Specification for the focal distance, lens-to-screen distance, FoV (H: horizontal, V: vertical), and resolution of various VR devices (ppmm: pixels per millimeter; dpi (or ppi): pixels per inch; ppd: pixels by degree). All specifications given are for one eye only. Technical characteristics are most of the time not disclosed by the manufacturer, and are either retrieved from 3rd party analysis (see table notes) or marked in the table by a question mark when the values are not known.

P = 2•D•tan 1 ′ 2 M . model f d l M/D display (

Emulating a Wall

The emulation of a wall display in VR can follow a simple procedure: we use a rectangle of the same size as the wall (the virtual wall) where we render the same scene that is rendered in the physical wall using physical dimensions in the VR space. Next, we are interested in calculating under which circumstances the users can see details rendered on this virtual wall. For this purpose, we can use the ppd of the HMD (that is, in general, provided by the manufacturers) to compute the size of a pixel when the virtual wall is at a distance d v of the user camera UC:

2 • d v • tan 1 ′ 2 ppd (6)
and in turn, compute the dpi of the virtual wall at a given distance of the UC. It should be noted that this size and dpi depend on the distance from the UC to the emulated wall.

For instance, the dpi of a virtual wall at 1 m of the UC, a comfortable distance to work with a wall, is of 24 dpi with the Vive Pro, 26 dpi with the Varjo XR-1 (standard display), of 36 dpi with the Vive Pro 2, of 44 dpi with the Vajo XR-3 (standard display), and should have, a priori, an "optimal" dpi of 87 with the Varjo XR-1 and Varjo XR-3 focus displays.

THE CLASSIFICATION TASK

Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] compared an ultra-wall (5.5 m × 1.8 m at 100 dpi) with a desktop (30" display at 100 dpi) in a classification task that abstracts various concrete classification and scheduling tasks (such as classifying brain scans and schedule the CHI 2013 conference). The scene rendered for the task has the size of the ultra-wall and is divided in 32 containers, each containing at most six discs with a letter, see Figure 1. The task consists of moving the discs so that all the containers contain discs with the same letter. The task starts with a partially classified scene where, in each container, there is a majority of discs with the same letter, which are shown in green, and the participants have to move miss-classified discs, shown in red, so that at the end all discs are green (correctly classified).

The difficulty of the task is controlled by the number of different letters (the classes). Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] consider an Easy task with two letters (two classes: "C" and "D") and a Hard task with four letters (four classes: "H", "K", "N" and "R").

The most important factor in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] is the size of the letters (factor LETTERSIZE) that represents information density. Small letters correspond to a large amount of information (e.g., a lot of text can be displayed), and large letters represent a low amount of information. Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] use three letter sizes:

• A large letter size, Large, 15.5 × 20 mm letters, that has been chosen so that (i) no virtual navigation is needed with the desktop: when the scene is scaled to be fully contained in the desktop screen the letters are easily readable; and (ii) no physical navigation is needed for the wall either: all the letters can be easily seen when the user stands in front of the wall at a distance of about 2 m. • A small letter size, Small, 1.8 × 2.3 mm letters (usual desktop 12pts font). In this case, to perform the task, the participants had to navigate the scene, either virtually using panning and zooming with the desktop, or physically by moving in front of the ultra-wall.

• A medium letter size, Medium, twice the small size, that we will not consider in our studies.

The main result of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] is that for large letters , where almost no navigation is needed, the desktop is faster than the ultra-wall, and that for small letters, where a lot of virtual or physical navigation is needed, the ultra-wall is faster than the desktop (35% faster for the hard task).

The classification Task in VR

To "replicate" the classification task in an HMD, we could emulate a wall in virtual reality: we use a rectangle of the same physical size as the wall (the virtual wall) where we render the same scene using physical dimensions (see Figure 1).

For the Large letter size condition, letters are large enough to be easily seen when the user camera is at 2 m of the virtual wall, and, a priori, a participant can perform the task without moving from their starting position. We consider this condition for testing whether the basic input interactions could impact the results: we expected no large differences between the different DEVICE conditions we will test. The Small letter size condition is more interesting. Indeed, here a letter starts to be readable when it is rendered (in the VR HMD) in a box with a width of at least 4 pixels and clearly readable when rendered in a 5 pixels wide box (Figure 6). The width of a letter is 1.8 mm, and thus, we need to have pixels of at least 1.8 4 = 0.45 mm, which correspond to a wall at 56 dpi and an optimal distance of 1.55 m.

With the Vive Pro, Equation 6shows that the user camera UC should be at a distance of about 44 cm of the virtual wall so that a user can see the letters (with a Vive Pro 2 we estimate this distance to 64 cm, and a little bit more with the standard display of the Vajo XR-3).

With an ideal HMD at 60 ppd, this distance is obviously of 1.55 m, and thus, in theory, with the Varjo XR-1 (and XR-3) and its focus display, this distance can be estimated to 1.55 m (as Varjo claim 60 ppd on the focus display). However, this is true only on a small part of the field of view, and for the rest of the field of view, a letter will be seen when the user camera is about 46 cm (XR-1) or 67 cm (XR-3) of the virtual wall.

Thus, we consider a special condition Simulated where we try to simulate an optimal VR HMD at 60 ppd. This is possible because of the nature of the classification task that considers isolated letters to represent information density. We, thus, just have to scale up the size of the letters to simulate an HMD with a higher ppd. For instance, to simulate an ideal HMD at 60 ppd we just have to scale the letters by a factor of 3.5, allowing a user to see the letters at a distance of 1.55 m in the Vive Pro. We consider such a condition in our first experiment. Moreover, to understand the impact of scaling information more globally, we also considered an additional condition Scaled where we scale the entire wall (not just the letters), by a factor of 3.5. This results in a virtual wall of 19.25 m × 6.3 m. The width of a small letter is then of 6.3 mm and is displayed with about 4 pixels when the wall is at a distance of 1.55 m from the UC, allowing, a priori, participants to perform the classification at a reasonable distance of the wall.

However, given the size of this wall, virtual navigation is needed to perform the task (e.g., to reach the top of the wall).

Virtual Navigation Techniques

To perform the classification task, the participants must move the discs with miss-classified letters, and navigate in front of the scene that is made up of the disk containers. Following the spirit of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], we decided to use the standard controller associated with our HMD and ray-casting for pointing. Pick-and-drop [START_REF] Rekimoto | Pick-and-drop: a direct manipulation technique for multiple computer environments[END_REF] is used to move a disc: a click with the pad of the Vive controller picks a disc, then the disc follows the ray on the plan of the virtual wall, and another click drops it in a container. Using pick-and-drop avoids long drags, which are not practical (especially with a wall), and also allows to use the controller to perform other actions during the pick-and-drop.

As explained in the introduction, in VR, we want the users to work without the need for a large physical space (like the big rooms needed to install actual Ultra-walls). we consider users working in more confined spaces sitting comfortably on a chair. Nevertheless, for our seated users to be able to "move" and see the content rendered in the virtual walls, we need virtual navigation techniques.

We consider two navigation techniques. For both techniques, the users can control the distance between them, i.e., the (UC), and the virtual wall (z translation) by using horizontal scroll on the pad of the Vive controller (Figure 7-(a)), similarly to how users zoom in and out using their mouse wheel. We consider two basic techniques for xy navigation (virtual wall plan): • Drag: by pressing the pad and moving the Vive controller, the users can move the wall on its plan (i.e., dragging in all the directions following the ray-casting of the controller, without modifying their distance to the wall), similarly to how users can drag a scene or a document in a desktop when pressing a mouse button. Figure 7-(b).

• Teleport: The users point on the wall and click using the trigger of the Vive controller (Figure 7-(a)-right). This teleports the users (UC) in front of this point without modifying their distance to the wall. This specific teleportation technique is more effective than classical teleportation (e.g., pointing on the floor), especially in the case of a huge wall, as it directly positions the users at the desired position in front of the wall. Figure 7-(c).

Thus, the goal is that, as observed in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], the users will choose an appropriate distance to the wall to see enough details (the letters) using vertical scroll on the pad and then will navigate at a constant distance of the wall in the same plan of the wall (Teleport) or move the wall in its plan (Drag). Note that both techniques can be used during a pick-and-drop.

We consider the two above techniques as they are standard for interacting in VR: (i) navigation in the virtual world with Teleport is common, efficient [START_REF] Bowman | Travel in Immersive Virtual Environments: An Evaluation of Viewpoint Motion Control Techniques[END_REF][START_REF] Bozgeyikli | Point & Teleport Locomotion Technique for Virtual Reality[END_REF] and reduces motion sickness [START_REF] Jacob Habgood | Rapid, Continuous Movement Between Nodes as an Accessible Virtual Reality Locomotion Technique[END_REF]; (ii) and moving/dragging objects using drag with Drag is also standard in VR and is the basic technique for navigating maps or documents in GUI.

FIRST STUDY: VIRTUAL NAVIGATION, SCALE AND SIMULATE

In this first study, we set out to compare how the different virtual walls perform and if the performance conforms to our theoretical calculations. We run the classification task in a Vive Pro (when we ran the experiment, the Vive Pro was the commercial HMD with the higher resolution). with the participants seated on a chair and using the virtual navigation described in the previous section, factor NAVTECH with two levels Teleport and Drag. However, during early testing we found that performing the task with the Vive Pro for the Small letters condition was really tedious (recall that the UC should be at about 44 cm of the virtual wall), and we decided to remove it from the study.

Thus, for the Small letters condition, we consider the two conditions Scaled and Simulated described in the Classification section. They allow to perform the task at a reasonable distance from the wall (1.55 m in theory and 1 m considering the results of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]). The Scaled condition takes advantage of the flexibility of VR to emulate any kind of wall and it allows to overcome the resolution issue by scaling the wall (and thus the letters). The Simulated condition allows to jump into the future to have an idea of the performance of an ideal HMD regarding resolution. Thus, our second main factor is DEVICE with two levels Scaled and Simulated.

We used the exact same tasks as in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] (same disc-letters layouts), with the goal to compare our results with those of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]. Thus we do not consider an ultra-wall condition in this first experiment and our virtual wall has the same base size as the wall of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] (multiplied by 3.5 for the Small-Scaled condition).

Note that for Large letters, there is no problem performing the task with the Vive Pro with a virtual wall of the same size as the wall of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] (even with no navigation). So with Scaled we will not scale the wall for the Large letters.

In practice, Scaled can be seen as an adaptive virtual wall that scales the wall when needed. Thus, for Large letters, the conditions Scaled and Simulated are equivalent, and thus, we only run it as one single condition (to avoid running both Scaled and Simulated for Large letters and tire our participants). In addition, we are mainly interested in the Small letters condition; the Large letters are mainly useful to compare the input devices later in this paper (the task is, under this condition, a repetition of pointing tasks on large targets: discs and containers).

Hypotheses

One important goal of this experiment is to validate the computation presented in the two previous sections. Thus, our first hypothesis relates to these computations and states that:

(H 1 ) For the Small letters conditions, participants will perform the task at a virtual distance of about 1 m from the virtual wall, distance predicted by our calculations and Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF].

Regarding navigation techniques, we expect different performance. With a huge wall (like Scaled) that needs a large amount of navigation over big distances, we may expect that teleportation will be more efficient. But when the amount of navigation needed is not as much (for example, for Simulated the wall size is smaller) we may expect that the better control that Drag provides will allow for better performance. However, this is an assumption given the nature of the techniques and navigation needed, and it is not supported by some related work or theory. Thus our next hypothesis is:

(H 2 ) When considering navigation NAVTECHs, for Scaled participants will be faster with Teleport than with Drag; and for Simulated, participants will be faster with Drag than with Teleport.

As Scaled is larger it will require more navigation than Simulated, thus we could expect that:

(H 3 ) When considering DEVICEs, participants will be faster with Simulated than with Scaled. This only applies to Small letters (as the Large is identical across devices).

Moreover, by design and given the results of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], we predict that participants will be faster with Large than with Small and faster with Easy than with the Hard.

Finally, we expect that when we compare with the results of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]:

(H 4 ) We will found only small differences between Simulated and the result of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] for the WALL in the Small letters condition.

Participants

We recruited 12 volunteers (4 women, 8 men) aged from 20 to 31 (mean 25.2, median 24.5). They were all right-handed, were not color blind, and had normal vision. Moreover, all the participants had already used a HMD, and they evaluated their familiarity with VR with a median of 3 (on a 1-5 Likert scale).

Apparatus

We used a HTC Vive Pro with one controller. Participants were seated in a fixed chair to avoid motion sickness, but they could move their hands, their head, and possibly their torso. The experimental software was written using Unity 3D with the SteamVR plugin.

Design and Procedure

The experiment is a 2 × 2 × 2 × 2 mixed-factorial within-participants design with factors:

• NAVTECH: Drag and Teleport;

• DEVICE: Scaled and Simulated;

• LETTERSIZE: Large and Small;

• DIFFICULTY: Easy and Hard.

All factors are crossed but as we discussed, (Scaled, Large) and (Simulated, Large) are the same condition, and thus DEVICE × LETTERSIZE leads to 3 conditions: (Scaled∼Simulated, Large), (Scaled, Small) and (Simulated, Small)

The experiment was split into two sessions (separated by one or two days), one for Drag and the other for Teleport and the presentation order was counter-balanced. A session started with four training tasks, and then the participants performed two times the task for each of the six DEVICE × LETTERSIZE × DIFFICULTY conditions (i.e., 2 repetitions).

The presentation order of these conditions was counter-balanced among the participants using a 6-Latin square.

We use the exact same classification tasks (discs layouts) as the one used in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF].

Participants started by signing a consent form that highlighted the risks of cybersickness and the operator verbally warned participants about cybersickness and urged them to immediately stop the experiment if they felt bad (none of them stopped the experiment). Then, the participants provided demographic information and read instructions to perform the task as fast as possible and to not drop a disc in a wrong container. At the beginning of each task, the user camera was positioned in the middle of the virtual wall at 2 meters of distance.

At the end of each session, the participants had to grade, on a 5-scale Likert, their perceived efficiency, enjoyment, physical fatigue, mental load, and motion sickness Moreover, participants had to rank the two navigation techniques at the end of the second session. A session lasted about 40 minutes.

Results

We first check (H 1 ), then we focus on our main measure, the task completion time (TCT), and report on the questionnaire.

Virtual distance to the virtual wall. Figure 8 shows the average virtual distance from the wall by condition (in all the bar-charts, error bars show the 95% confidence intervals). We observe that for Large letters participants did not have to come closer to the wall. More importantly, for the Small letters condition, we can observe that the distance between the UC and the virtual wall is close to 1 m. Thus, (H 1 ) is supported.

2.1 2 1.9

1.9 Task Completion Time. We checked for outliers and observed that, as in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], 95% of the trials were within 15% of the mean completion time per condition. Only one trial was more than 25% slower (namely 39%), and we removed this trial for the analysis (a Teleport-Scaled-Small-Hard trial). Normality tests did not show any evidence of non-normality.

We used the same statistical method as Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], repeated measure ANOVA, and Holm corrected (paired) t-tests.

Figure 9 shows the task completion time for each NAVTECH, by DIFFICULTY grouped by DEVICE×LETTERSIZE, and the results of the ANOVA for the model (Participant as a random factor; for the analysis we duplicate the Large data for Scaled and Simulated as they are the same condition): TCT ∼ NAVTECH × DEVICE × LETTERSIZE × DIFFICULTY Although Teleport seems slightly faster than Drag, there is no significant effect of NAVTECH, nor significant interactions involving NAVTECH on TCT. Thus, (H 2 ) is not supported in terms of performance in terms of navigation technique.

We have a significant effect of DEVICE on TCT, Simulated being faster than Scaled. As the data are the same for Large letters for Scaled and Simulated, we have a significant DEVICE×LETTERSIZE interaction effect as the difference between Scaled and Simulated could happen only for Small letters. Thus (H 3 ) is supported. Note also that we have a significant DEVICE×DIFFICULTY interaction on TCT, and indeed, the difference between Scaled and Simulated is larger for the Hard task (Cohen d = 3.07) than for the Easy task (Cohen d = 2.18).

As expected, we observe large significant effects of LETTERSIZE and DIFFICULTY on TCT: (i) participants have been faster with the Easy tasks than with the Hard tasks for all the NAVTECH × DEVICE × LETTERSIZE conditions (all p's<0.001); (ii) participants have been faster with Large than with both Simulated-Small and Scaled-Small (all p's<0.001).

We can also observe a significant LETTERSIZE × DIFFICULTY interaction effect. Indeed, the differences between the LETTERSIZE conditions were relatively bigger for the Hard tasks than for the Easy tasks (Cohen d's differences of ∼1).

Subjective Results. Figure 10 shows the results of our questionnaire. Overall, participants found the navigation techniques effective and enjoyable, but reactions were more mixed regarding fatigue and mental load (probably because some found the task challenging). Three participants also said they suffered from motion sickness but did not stop the experiment. We notice that overall Teleport has been slightly better rated than Drag, in particular regarding fatigue, but, indeed, we could not measure any significant (or marginal) differences (all questions, Wilcoxon paired signed rank tests).

We also asked the participants to rank the navigation techniques for both small letter conditions. In the case of Simulated-Small, 8 participants (over 12) preferred Drag, as for Scaled-Small, 9 participants preferred Teleport. Thus, regarding preferences, (H 2 ) has an agreement score of 17 over 24 (p=0.064).

Comparing with Liu et al.

Now, we compare the data of our experiment with the data of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]. To simplify, we will consider only the data for Teleport, as the results were slightly better than for Drag. In the above experiment, we recall that we used the same procedure, design, disc layout, order, etc., as in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]. This allows us to merge and compare our data with that of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] (note we removed the Medium data from Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] as we did not run this condition).

To summarize, we consider a [4×2×2] mixed factorial design with factors:

• DEVICE: WALL and DESKTOP (from Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]) and Scaled and Simulated (from our experiment);

• LETTERSIZE: Large, Small (both experiments);

• DIFFICULTY: Easy and Hard (both experiments).

with DEVICE considered as a between-participants factor. Note that, obviously, physical navigation was used in Liu et al.

[30] experiment for the WALL condition, while we used virtual navigation in our VR conditions.

Results for TCT can be observed in Figure 11, as well as the ANOVA for the mixed model (participant as a random factor): TCT ∼ [DEVICE] × LETTERSIZE × DIFFICULTY. Again, obviously, we have a strong effect of LETTERSIZE and DIFFICULTY on TCT, and a significant LETTERSIZE × DIFFICULTY interaction.

We focus on the significant effect of DEVICE and the Small LETTERSIZE condition. Given the effect size of the DEVICE × LETTERSIZE interaction effect, we also consider DIFFICULTY in each case:

• Small letters -Easy task: Scaled is slower than all the other DEVICE: DESKTOP (p = 0.0650, d = 1.22), Simulated (p = 0.0096, d = 1.44), and WALL (p = 0.0224, d = 1.64). For Simulated we found no significant difference with the DESKTOP (p = 0.612) and the WALL (p = 0.8166). Recall that Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] find that the WALL was faster than the DESKTOP (p = 0.0396, d = 1.11).

• Small letters -Hard task: Scaled is also slower than all the other DEVICE, the difference is significant with Simulated (p = 0.0012, d = 2.32) and WALL (p = 0.0012, d = 2.17), but not with DESKTOP (p = 0.8166). For Simulated we find that it is significantly faster than the DESKTOP (p = 0.0050, d = 1.77), but the difference with the WALL is not significant (p = 0.8166). Again, we recall that Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] find that the WALL is significantly faster than the

DESKTOP (p = 0.0090, d = 1.61).
Thus, (H 4 ) is supported, and to summarize, our results suggest that an HMD with a resolution matching human vision can be as efficient as an ultra-wall in tasks that involve high-density information (Small letters).

Note that for Large letters, participants have been faster in our experiment than in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] experiment. We explain this result by the difference in the input devices (Vive active tracking in our experiment vs. Vicon passive tracking and mouse) and that our large letters were, in fact, larger than the ones in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF].

FOLLOW-UP STUDY: PHYSICAL WALL VS. HMDS, USING PHYSICAL NAVIGATION

In the previous study, we had seated participants and used virtual navigation (on propose), so as to avoid using an exact emulation of a wall, that would require otherwise a lot of walking in the Small condition with the Vive Pro (our initial tests caused too much motion sickness). However, when considering physical navigation, some tests suggested that running the Small condition with the Vive Pro was reasonable. A second limitation of the first experiment is that we did not compare the results of our HMDs condition directly with an ultra-wall, rather we used the results of a previously published experiment.

Thus, in this second study, we compare a 140 dpi ultra-wall of about the same size as the wall of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] (Wall) with emulations of this wall in a Vive Pro (Vive) and in a Varjo XR-1 (Varjo) using physical navigation. We used physical navigation (which is required for the physical Wall as we cannot teleport), to ensure that any effects found are due to the rendering and not the navigation techniques. And to ensure a fair comparison (since walking is required), we emulated walls in the HMDs that are identical in size to the physical Wall and not bigger.

We also wanted to consider the Simulated condition. Unfortunately, we made a mistake and scaled the letters by a factor of 4.2 (and not 3.5), making the classification task too easy. However, as we will see, this led to an interesting result. We denote by Simu-4.2 this condition (for Large we used the same size as Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF], i.e., we did not scale).

Thus, in this study, our primary factor is DEVICE with four levels: Wall, Vive, Varjo, and Simu-4.2. We introduced the Varjo XR-1, an HMD that includes a focus display with a (claimed) perfect-resolution.

We used again the classification task (see Classification section), with four letter classes: "H", "K", "N" and "R" (i.e., we only consider the Hard task; we do not consider the Easy task as it led to smaller effects than the Hard task, in both Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] and our own first study). Thus, our second factor is LETTERSIZE with two levels Large and Small (as in the first study). We recall that as we are studying the effect of resolution, we are mainly interested in the Small size condition. The Large size is useful to see whether the different DEVICEs perform equally well for the basic interactions (e.g., pick-and-drop): we expected very similar performance for the different DEVICEs in the Large condition.

We stopped the experiment after 6 participants once we noticed our error in the simulated condition. Nevertheless, we report the results of the 6 participants here, as they present interesting tendencies, and our mistake led to an interesting observation regarding the subjective results.

Hypothesis

Our analysis in the Background section and the above experiment (H 4 ) suggest that: (H 5 ) For the Small letters, the Wall will out perform the Vive and the Varjo.

We can also hope that the focus display of the Varjo will be beneficial for small letters: (H 6 ) For the Small letters, the Varjo will be faster than the Vive.

Moreover, as explained above, we expect that for the Large letters, we will found only small differences among the DEVICE. Finally, our original hypothesis regarding Simu-4.2 was that we would observe only small differences between the Wall and Simu-4.2. However, given the mistake we made, it should be expected (at posteriori) that Simu-4.2 should be faster than the Wall (bigger letters thus easier task).

Participants

We recruited 6 volunteers (5 men, 1 woman) aged from 24 to 45 (mean 30, median 27) with normal or corrected-to-normal vision (over the 12 participants initially planned). Participants were HCI researchers, engineers or graduate students in computer science. Most of them already used an HMD and a wall.

Apparatus

For the wall display (Wall), we used a 5.50 × 1.80 m display with a resolution of 30 720 × 8 640, composed of 8 × 4 = 32 4K LCD displays and driven by 8 workstations (140 dpi, see Figure 4, note that the displays of this wall also support a resolution 8K, doubling the resolution and the dpi of the wall). For the VR headsets, the Vive Pro (Vive) and the Varjo XR-1 (Varjo), we ran the experiment in the same room as the wall display room. The Varjo XR-1 was, when we ran the experiment, the best HMD available regarding resolution. We chose the Vive Pro as it is a standard, consumer-grade headset with a good resolution, and we used it in the first study. We run the erroneous simulation condition (Simu-4.2) in the Vive Pro because it is more comfortable to wear than the Varjo XR-1.

For the input, we use one Vive controller (participants used the trigger of the controller for pick-and-drop), but we perform the tracking using a Vicon system (for all the DEVICE conditions). We used physical navigation for all conditions, with the cable for the VR headsets suspended so the participants would not trip on it. The software used was Unity 3D with the same base code for the HMDs and the wall.

Experiment Design & Procedure

Design. The experiment is a full factorial DEVICE×LETTERSIZE within-participants design. We blocked by DEVICE, and fixed the LETTERSIZE presentation order to Large first, followed by Small, as we are mainly interested in effects due to resolution (that we expect with Small letters). In the initial 12 participants' design, we counter-balanced the device condition using a Latin square. For each device, participants had 6 trials: 2 training trials with each LETTERSIZE condition in the order defined above, then 2 × 2 experiment trials in the same order. We used the exact same initial letter dispositions as in Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] and the previous experiment to allow for a comparison of results.

Putting everything together

Figure 14 shows the task completion time for the Large and Small letters conditions (Hard task) for all the "device" conditions considered in this paper: Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] and our two studies. Beside the differences already noticed in the two previous sections, we can note that for he Small letters, the Simulated condition (study 1), the Wall condition (study 2), and the WALL condition (Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]) exhibit close performances, and clearly outperform the DESKTOP (Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]) and our realistic HMDs condition (Scaled, Vive, Varjo). Note that these four last conditions have close performance, too. These results confirm our analysis of the capacity of HMDs to emulate an ultra-wall: HMDs are not yet capable to emulate physical walls, but "perfect" future HMDs will likely arrive at similar performance.

CONCLUSION, DISCUSSION AND FUTURE WORK

Our results suggest that, as expected, current "standard" HMDs do not have the needed resolution to compete with an ultra-wall in a 2D task consisting of manipulating data of "high information density." They also suggest that the Varjo technology, which consists of adding a focus display with a very high resolution in the center of the field of view, is of limited utility in a search task involving small details on a large surface (but they can be undoubtedly helpful for reading small texts, for instance).

Moreover, our results also suggest that a hypothetical perfect-resolution HMD (60 ppd in the field of view) would be competitive in terms of performance. However, the second study suggests that users may prefer a physical wall anyway.

Indeed, participants were faster and traveled less distance with the Simu-4.2 condition than with the Wall condition (Simu-4.2 that was easier due to our error), but despite that all participants preferred the Wall condition over the Simu-4.2 condition.

A limitation of our work is that the second study has only 6 participants. However, we believe that the results are robust.

For instance, we find similar results to Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] for the walls, and the results are consistent with our theoretical prediction. Of course, it would be interesting to run the second study with 12 participants and with the Vive Pro 2 and the Varjo XR-3, the current best resolution HMDs (about 30 ppd, only half of what is needed for a perfect-resolution HMD).

Nevertheless, given our theoretical analysis and our empirical results that are consistent with this analysis, we believe that even these new HDMs will not reach the performance of a physical ultra-wall, but rather fall somewhere between the performances seen in the HMDs tested in this article and that of the ultra-wall.

In fact, it would be more interesting to test an HMD that both provides perfect resolution in its field of view and a view on reality (e.g., a super Hololens or an ultra VR HMDs with video-see-through). Apple announced such an HMD, and the classification task could be an interesting benchmark to evaluate its resolution and the quality of its view on reality (for example comparing a virtual wall and a physical one, both seen through the HMD).

However, HMDs should not only be tested regarding their resolution. For instance, wall displays are mainly useful for collaborative work, and a view on the reality is a decisive advantage in a co-located collaborative context. This does not necessarily mean that virtual walls are not useful even in these contexts. Results from James et al. [START_REF] James | Evaluating the Extension of Wall Displays with AR for Collaborative Work[END_REF] show that pairs did not hesitate to interact collaboratively with AR surfaces even in the presence of a wall display. We think that future work should compare ultra-walls with high-resolution AR headsets (e.g., the Apple Vision Pro) in collaborative tasks such as the classification task of [START_REF] Liu | Shared Interaction on a Wall-Sized Display in a Data Manipulation Task[END_REF] and in tasks that require less interaction and more high-level decision making and planning such as those of [START_REF] Mikkel | Up Close and Personal: Collaborative Work on a High-resolution Multitouch Wall Display[END_REF][START_REF] Liu | CoReach: Cooperative Gestures for Data Manipulation on Wall-sized Displays[END_REF][START_REF] Wallace | Creating Your Bubble: Personal Space On and Around Large Public Displays[END_REF], but also continue to study contexts where the two types of devices can be combined to provide richer and more flexible collaborative environments [START_REF] James | Personal+Context Navigation: Combining AR and Shared Displays in Network Path-following[END_REF][START_REF] Reipschläger | Personal Augmented Reality for Information Visualization on Large Interactive Displays[END_REF].

SUPPLEMENTAL MATERIAL

Experimental data and the discs layouts of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] are available as supplemental material and online at https: //www.lri.fr/~chapuis/ihm-2024-data/. We thank the authors of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] for sharing their data.

Fig. 1 .

 1 Fig. 1. Virtual wall: A user moving a disc while performing the classification task of Liu et al. [30] on a 5.5 m × 1.8 m virtual wall display that is rendered in an HMD (large letters: low information density condition).

Fig. 2 .

 2 Fig. 2. Simplified scheme for the monocular eye looking at a physical screen. w = 2•d •tan µ 2 .

Fig. 5 .

 5 Fig. 5. Simplified scheme for optics of a VR headset. f : focal distance, d e : distance beteween the eye and the screen, d i : distance between the lens and the virtual image.

Fig. 6 .

 6 Fig. 6. Mapping the letter N into a 4x4 and a 3x3 matrix.

Fig. 7 .

 7 Fig. 7. (a) HTC vive controller. (b) The Drag technique. (c) The Teleport technique.

Fig. 9 .

 9 Fig. 9. (Left): Mean task completion time in seconds for each NAVTECH by DIFFICULTY grouped by DEVICE × LETTERSIZE. (Right): Degree 2 factorial ANOVA with participant as random factor for the TCT measure.

Fig. 11 .

 11 Fig. 11. (Left) Mean task completion time in seconds for each DEVICE by DIFFICULTY grouped by LETTERSIZE. (Right) Mixed factorial ANOVA with participant as random factor for the TCT measure (DEVICE between factor).

Fig. 14 .

 14 Fig. 14. Task completion time for the Small letters conditions (Hard task) for the devices conditions from Liu et al. [30] (DESKTOP, WALL), our 1st study (ViveTeleport, Scaled and Simulated in the Teleport condition) and our 2nd study (Vive, Varjo, Wall and Simu-4.2).

  By abuse of language, we call this smallest entity a visual pixel.

	Device	dpi pixel size optimal visual distance
	Smartwatch (Apple watch s. 8) 330 0.0769 mm	26.4 cm
	Smartphone (OnePlus 10T)	394 0.0640 mm	22.0 cm
	Tablet (IPad)	264 0.0960 mm	33.0 cm
	Laptop (15", FullHD)	145 0.1750 mm	60.2 cm
	Our Wall (4K screens)	140 0.1800 mm	61 cm

  Fig. 8. distance from the wall for each NAVTECH by DIFFICULTY and grouped by DEVICE×LETTERSIZE.

				Drag	Teleportation
											Effect	n, d	F n,d	p	η 2 G
		Large	Scaled-Small	Simulated-Small			NAVTECH	1,11 3.68	0.0813 0.02
		300									DEVICE	1,11	103 < 0.0001 0.41
											LETTERSIZE	1,11	249 < 0.0001 0.79
	Task Time (s)	100 200									DIFFICULTY NAVTECH×DEVICE NAVTECH×LETTERSIZE DEVICE×LETTERSIZE NAVTECH×DIFFICULTY	1,11 1,11 4.60 228 < 0.0001 0.51 0.0515 0.01 1,11 1.13 0.2761 0.01 1,11 104 < 0.0001 0.41 1,11 2.64 0.1323 0.01
											DEVICE×DIFFICULTY	1,11 30.1	0.0002 0.12
		0									LETTERSIZE×DIFFICULTY 1,11	175 < 0.0001 0.36
		Easy	Hard	Easy	Hard	Easy		Hard
						Drag		Teleportation
				Large	Scaled-Small	Simulated-Small
			Dist To Wall (m)	0.0 0.5 1.0 1.5 2.0 2.5		1.1	1	1	1.1	1	1	1	0.9
				Easy	Hard	Easy	Hard	Easy	Hard

  Results of the questionnaire for Efficiency, Enjoyment, Physical Fatigue, Mental Load, and Motion Sickness (the four questions of Liu et al.[START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF] that come from the NASA-TLX, plus a question regarding motion sickness). Values on the right (in green) are "better" for all the questions.

					Effectiveness qnl...c.2..						Enjoyment qnl...c.3..
		Telep.	0%		17%			83%	Telep.	8%	17%	75%
		Drag	8%		17%			75%	Drag	8%	33%	58%
			100	50	0 Percentage Fatigue qnl...c.4..	50	100			100	50	0 Percentage Mental qnl...c.5..	50	100
		Telep.	8%		25%			67%	Telep.	50%	17%	33%
		Drag	33%		17%			50%	Drag	42%	42%	17%
			100	50	0 Percentage	50	100	Sickness qnl...c.6..		100	50	0 Percentage	50	100
					Telep.	25%		0%			75%
						Drag	25%		8%			67%
	DESKTOP [30] Fig. 10. Large	Scaled	100 Simulated Small	50 WALL [30]	0 Percentage Effect	50	100	n, d	F n,d	p	η 2 G
									DEVICE	3,44 13.0 < 0.0001 0.25
	Task Time (s)	100 200							LETTERSIZE DIFFICULTY DEVICE×LETTERSIZE DEVICE×DIFFICULTY LETTERSIZE×DIFFICULTY	1,44 1,44 3,44 34.0 < 0.0001 0.30 420 < 0.0001 0.72 300 < 0.0001 0.55 2,44 9.16 < 0.0001 0.10 1,44 148 < 0.0001 0.35
									DEVICE×LETTERSIZE×DIFFICULTY 4,44 9.05 < 0.0001 0.09
		0
		Easy		Hard		Easy		Hard

ACKNOWLEDGEMENTS

This research was supported by French government funding managed by the National Research Agency (ANR) under: (i) the Investments for the Future program (PIA) grant ANR-21-ESRE-0030 (CONTINUUM); and (ii) the Collaborative Research Project (PRC) grant ANR-21-CE33-0022 (InterPlay).

Procedure. Each participant took part in two sessions, on two separate days, seeing 2 (out of the 4) device conditions in each of the two sessions. A break was enforced in-between conditions inside a session. At the beginning of each VR condition, we warned the participants about possible motion sickness discomfort. At the end of the second session the participants filled-in a questionnaire asking to rank the techniques (with possible ex-aequo) regarding their preferences:

overall, enjoyment, effectiveness, mental and physical load, and motion sickness.

Each condition started with a vision test to determine what is a comfortable viewing distance for each one of the conditions, and if it is close to our theoretical calculations. The operator asked the participant to look at a letter of Small size on the wall, and to then move back, one step at a time, until they hit the limit where they could not tell the letter anymore (at each step, we changed the letter shown, chosen among "H", "K", "N" and "R"). The resulting distance was recorded. The operator then explained the interactions and the task's goal, ensuring the participant felt comfortable.

Next, participants moved onto the classification tasks. Before each trial, the participants were placed at a 2 m distance from either the physical wall or the virtual wall in the VR conditions. The 2 m distance was chosen as it was, a priori, a comfortable distance to perform the Large letters task.

Results

Distance to the Wall: Vision Test and Classification Trials. were able to see the Small letters at 50 cm with the Vive, which is slightly more than what we predicted (45 cm). For the Varjo, the participants needed to be at 1.20 m from the letters to see them, which is 35 cm less than predicted. This suggests that the focus display of the Varjo XR-1 does not reach the claimed 60 ppd (it is closer to 50 ppd).

The result for the physical Wall is about 1.9 m distance, 35 cm more than the expected value of 1.55 m. We have no explanation, but we will see that in practice, the participants performed the classification task for the small letters at about 1 m from the screen. For Simu-4.2, the distance is about 2.2 m, more than our expectation of 2 m.

For the classification task, results differed. Figure 12-(b) shows, for the Small condition, the average distance of the participant to the virtual or physical wall during a trial. Note that the distance to the wall for the physical Wall was of about 1 m (far smaller than the 1.9 m of the vision test and very close to the results of Liu et al. [START_REF] Liu | Effects of Display Size and Navigation Type on a Classification Task[END_REF]). This trend to have a smaller distance than the vision test is also true for Simu-4.2, but the difference is not as strong. In contrast, the distance during the classification task is close to the vision test distance for the Vive. Finally, despite its focus display, this distance for the Varjo is close to the one of the Vive, indicating the focus display did not really help in the classification task. Task Time. Figure 13-(a) shows the task time by DEVICE × LETTERSIZE conditions. For the Large condition, we can observe, as expected, that the task times are very similar. This suggests that the input system worked equally well for all DEVICE. For the Small condition, we can observe that (we did not report statistical tests as we have only 6 participants.

However, the results we state are supported by t-tests, as well as by Bayesian analysis).

• As expected, the physical Wall was faster than the Vive, but also faster than the Varjo (this was the case uniformly for the 6 participants, with clear differences between mean times). Thus (H 5 ) is supported. • The task time is similar for Vive and Varjo, making (H 6 ) unlikely to hold. This suggests that for a task where users have to move in front of a (virtual) wall and move their heads in all directions to see small details, the focus display of the Varjo is not really useful.

• Simu-4.2 was faster than the Wall (clearly for 5 participants, but one participant was 2 s faster with Wall). This is expected given the size of the letters with Simu-4.2 (that is by error larger than our model calculation).

Traveled Distance. Figure 13-(b) shows the average traveled distance by the participants during a trial by DE-VICE × LETTERSIZE conditions. For the Large letter condition, participants did not need to come close to the wall and did not need to travel too much. For the Small condition, the results confirm the results on the task time: task time is "positively correlated" with the distance to the wall to perform the task (far away is better), and the traveled distance during the task (shorter is better).

Subjective Results. Regarding their overall preferences, the six participants ranked the physical Wall condition 1st and the Simu-4.2 condition 2nd (4 participants ranked the Vive 3rd, and 3 participants ranked the Varjo 3rd, ex-aequo were allowed). Given the quantitative results above, it is not surprising that the participants preferred the Wall and the Simu-4.2 conditions over the other virtual walls. However, it is surprising and interesting that the six participants uniformly preferred the Wall condition over the Simu-4.2 condition, given that ex-aequo were allowed and that Simu-4.2 exhibits better quantitative performance than the Wall (uniformly: for the six participants).

We obtain the same results for enjoyment and effectiveness and similar tendencies regarding mental and physical load.

Only one participant reported a moderate discomfort regarding motion sickness. Thus, the subjective results suggest that participants appreciated more the physical aspect of the wall display compared to the virtuality of a headset.


Anisia Popescu
email: anisia.popescu@universite-paris-saclay.fr

Mathilde Hutin

Ioana Vasilescu

Lori Lamel

Martine Adda-Decker

STOP DEVOICING AND PLACE OF ARTICULATION: A CROSS-LINGUISTIC STUDY USING LARGE-SCALE CORPORA

Keywords: large corpora, voicing maintenance, automatic alignment, pronunciation variants, Romance languages

whether they are published or not. The documents may come

INTRODUCTION

Maintaining voicing requires sufficient transglottal airflow. In stops, air accumulates in the oral cavity, reducing the oral and subglottal pressure gradient which may lead to insufficient transglottal airflow and result in voicing loss. This is known as the Aerodynamic Voicing Constraint (AVC) [START_REF] Ohala | The origin of sound patterns in vocal tract constraints[END_REF][START_REF]Accommodation to the aerodynamic voicing constraint and its phonological relevance[END_REF]. Certain places of articulation are more compatible with voicing than others: It has been shown that voiced stops are more marked the more posterior the constriction location is [START_REF] Gamkrelidze | On the correlation of stops and fricatives in a phonological system[END_REF] (i.e., voiced labials and coronals are more frequent than voiced velars, making /g/ a frequent phonological gap [START_REF] Nikolaev | A typology of consonant inventory gaps[END_REF][START_REF] Sherman | Stop and fircative systems: A discussion of paradigmatic gaps and the question of language sampling[END_REF]). Initially this pattern was attributed to oral cavity volume [START_REF] Martinet | Economie des changements phonétiques[END_REF]: Pressure builds up faster in a posterior constriction location leading to faster voicing loss in velar stops.

Several studies [START_REF] Ohala | Passive vocal tract enlargement during voiced stops[END_REF][START_REF] Javkin | Towards a phonetic explanation for universal preferences in implosives and ejectives[END_REF] have since shown that the difference in cavity volume alone has a negligible effect on pressure differences, and that the pattern is more likely related to the compliant surface available for passive enlargement of the vocal tract: Stops with more anterior places of articulation have more soft tissue available for increasing the vocal tract volume (i.e., lowering the larynx, elevating the soft palate, advancing the tongue root, depressing the tongue body [START_REF] Westburry | Enlargement of the supraglottal cavity and its relation to stop consonant voicing[END_REF][START_REF] Rhotenberg | The breadth-stream dynamics of simple-released-plosive production[END_REF]).

However, most studies investigating voicing alternations as a function of place of articulation are done in controlled, small-scale laboratory studies (e.g., [START_REF] Pape | Stop and Fricative Devoicing in European Portuguese, Italian and German[END_REF][START_REF] Keating | Physiological effects on stop consonant voicing[END_REF]). Large scale cross-linguistic studies are scarce. A recent corpus study investigating the voicing of Danish stops in intervocalic position [START_REF] Pugaard-Rode | The rarity of intervocalic voicing of stops in danish spontaneous speech[END_REF] did not find the predicted pattern supporting an aerodynamic account: The highest rate of voicing was found for coronals, not velars. The present study aims to test whether velar stops are more prone to devoicing than their labial and coronal counterparts in the European variants of five Romance languages (French, Italian, Portuguese, Romanian and Spanish), using large corpora and automatic alignment with pronunciation variants.

METHODOLOGY

Corpora and devoiced stop detection

The corpus consists of more than 1000 hours of French, Italian, Portuguese, Romanian and Spanish journalistic speech from TV and radio shows.These corpora were acquired from the LDC and ELRA, or developed during international projects: IST ALERT for part of the data in French and Portuguese [START_REF] Da Silva | Lattice-based unsupervised acoustic model training[END_REF][START_REF]Interpolation of acoustic models for speech recognition[END_REF]; IRST for part of the data in Italian [START_REF] Després | The vocapia research asr systems for evalita 2011[END_REF][START_REF] Marcello | Development and Evaluation of an Italian Broadcast News Corpus[END_REF] and OSEO Quaero [START_REF] Lamel | Speech Recognition for Machine Translation in Quaero[END_REF][START_REF] Lamel | Multilingual speech processing activities in Quaero: application to multimedia search in unstructured data[END_REF] for all languages. Only European shows were kept in the final corpus. Manual transcription was available for all data, except for Romanian, which only had 7h of manually transcribed data; The reminder of the Romanian data was automatically transcribed using a speech-to-text system [START_REF] Vasilescu | Exploring pronunciation variants for Romanian speech-to-text transcription[END_REF].

An automatic speech recognition (ASR) system [START_REF] Lamel | On designing pronunciation lexicons for large vocabulary continuous speech recognition[END_REF] was used to carry out the forced alignment, matching speech segments to their orthographic transcription using language specific acoustic models and pronunciation dictionaries. A total of 486,366 bilabial, 1,557,644 coronal and 283,328 velar stops were labeled by the system across the five languages. Following [START_REF] Lamel | On designing pronunciation lexicons for large vocabulary continuous speech recognition[END_REF][START_REF] Adda-Decker | Pronunciation variants across system configuration, language and speaking style[END_REF], the dictionaries were enriched with equiprobable pronunciation variants for stop voicing (e.g., the Fr. beaucoup /boku/ 'a lot' had four possible pronunciations: [boku], [poku],

[bogu], [pogu]) allowing the system to choose the variant which best matches the language-specific acoustic models. This method has proven reliable and useful to the investigation of voicing and devoicing in several recent works (Romanian [START_REF] Hutin | Ongoing Phonologization of Word-Final Voicing Alternations in Two Romance Languages: Romanian and French[END_REF], French [START_REF] Jatteau | Gra[f]e!' Word-Final Devoicing of Obstruents in Standard French: An Acoustic Study Based on Large Corpora[END_REF], Spanish [START_REF] Ryant | Large-scale analysis of spanish /s/-lenition using audiobooks[END_REF][START_REF] Vasilescu | Exploring Temporal Reduction in Dialectal Spanish: A Large-scale Study of Lenition of Voiced Stops and Coda-s[END_REF][START_REF] Hualde | Lenition of intervocalic alveolar fricatives in catalan and spanish[END_REF], Italian [START_REF] Hualde | Lenition and phonemic overlap in Rome Italian[END_REF] or several Romance languages [START_REF] Vasilescu | Alternances de voisement et processus de lénition et de fortition: une étude automatisée de grands corpus en cinq langues romanes[END_REF][START_REF] Hutin | Synchronic Fortition in Five Romance Languages? A Large Corpus-Based Study of Word-Initial Devoicing[END_REF][START_REF] Hutin | When phonetics meets morphology: Intervocalic voicing within and across words in romance languages[END_REF][START_REF] Wu | Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages[END_REF]). The rates of devoiced variants detected by the system are given in Tab. 2 and illustrated in Fig. 1

Statistical analysis

Various phonetic and phonological factors have been shown to correlate with loss of voicing. The factors we are considering in this study are place of articulation (PoA: bilabial, coronal, velar), stop duration, language (French, Italian, Portuguese, Romanian, Spanish), position in the word (initial, medial, final), position in the syllable (onset, coda), phonotactic complexity (singleton, cluster) and voicing status of the previous and following segment (pause, voiceless, voiced). The data was modeled using a logistic regression and was largely inspired by the analysis in [START_REF] Pugaard-Rode | The rarity of intervocalic voicing of stops in danish spontaneous speech[END_REF]. The duration variable was log-transformed in order to reach a near normal distribution. Categorical variables were contrast coded as follows: A sum contrast was coded for binary variables (phonotactic complexity, syllable position), theoretically motivated Helmert contrasts were coded for the three-level variables (see below) and deviation coding was used for the five-level Language variable (i.e., each language was compared with the overall mean of the Language variable). Each term and their interaction with place of articulation were added as factors to the model. Since speaker information (speaker ID) was not available for all data, random factors were not included for the present analysis. Below we describe the theoretically motivated Helmert contrast coding: Based on previous literature, we make the following predictions for each of our model variables:

• PLACE OF ARTICULATION: higher rates of devoicing in velar than in labial and coronal consonants (velar > coronal > bilabial) [START_REF] Ohala | Passive vocal tract enlargement during voiced stops[END_REF] • STOP DURATION: higher rates of devoicing in longer stops [START_REF] Wu | Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages[END_REF] • LANGUAGE: no specific predictions • POSITION IN WORD: higher rates of devoicing in final position followed by initial and medial positions (final > initial > medial) [START_REF] Vasilescu | Alternances de voisement et processus de lénition et de fortition: une étude automatisée de grands corpus en cinq langues romanes[END_REF] • SYLLABLE POSITION:

higher rates of devoicing in coda position (coda > onset) [START_REF] Ségéral | Positional factors in lenition and fortition[END_REF] • PHONOTACTIC COMPLEXITY: higher rates of devoicing in clusters than in singletons (clusters > singletons) • VOICING ENVIRONMENT: higher rates of devoicing in pause or voiceless contexts (pause > voiceless > voiced) [START_REF] Wu | Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages[END_REF] 3. RESULTS

Results will be presented in two stages. First we report on overall tendencies for all variables (i.e., all five languages pooled together). We then present language-specific devoicing differences related to place of articulation. The reported patterns are given in Tab. 3 and illustrated in Fig. 2, which show the rates of devoiced stops detected by the system for each of our categorical variables (word position, syllable position, phonotactic complexity, voicing status in the previous and following segments) for all five languages.

Variable

Language-specific patterns

When modeling the velar and labial vs coronal contrasts per language (comparing each language with the overall mean of the LANGUAGE variable), we find that the velar contrast (velars compared to bilabials and coronals) is not significant for Italian, Spanish and French (p>0.1). Romanian and Portuguese pattern with the overall direction (velars tend to devoice less than bilabials and coronals), the effect being highest for Portuguese (Por: Est.: -0.106281, t ∼ -4.842, p<0.0001; Rom: Est.: -0.037202, t ∼ -2.204, p<0.05). When looking at contrasts between bilabials and coronals, we find that there is no significant difference in devoicing rates for Romanian and French (p>0.1). Portuguese has significantly higher rates of coronal than bilabial devoicing (Est.: 0.540493, t ∼ 25.003, p<0.0001). Spanish and Italian both show patterns that support aerodynamic accounts of voicing maintenance: Bilabials tend to devoice more than coronals in both languages, when compared to the overall mean (Ita: Est.: -0.417066, t ∼ -20.304, p<0.0001; Spa: Est.: -0.025293 t ∼ -2.090, p<0.05).

To sum up, velars do not devoice at higher rates than bilabial and coronal consonants: No significant effect was found for Italian, Spanish and French and the opposite trends (i.e., velars devoice less than bilabials and coronals) was found for Portuguese and Romanian. The labial vs coronal contrast seems to be more language-specific: In Italian and Spanish, bilabials devoice at higher rates than coronals, Portuguese shows the opposite trend (coronals > bilabials) and Romanian and French show no difference between bialabial and coronal devoicing rates when compared to the overall mean.

DISCUSSION

The present study investigates whether there are any differences in stop voicing maintenance based on place of articulation in the European varieties of five Romance languages. More than 1000 hours of journalistic speech were automatically aligned using pronunciation variants, which allow the system to automatically detect devoiced stops based on language-specific automatic speech recognition (ASR) systems. By analyzing the effect of place of articulation along with other phonetic and phonological factors, we find that our predictions based on aerodynamic accounts of voicing maintenance are not confirmed: Stops with more posterior constriction locations (velars) are not more prone to voicing loss. Instead, in line with previous studies [START_REF] Pugaard-Rode | The rarity of intervocalic voicing of stops in danish spontaneous speech[END_REF], we find that coronals tend to devoice at higher rates (a trend that is mainly driven by the high coronal devoicing in Portuguese). Several explanations can be put forward. First, coronals tend to be more frequent than bilabials and velars, and articulatory reduction phenomena (e.g., glottis spreading) tends to occur in more frequent environments. This is also the case for our data (there are twice as many coronal stops than velars and bilabials). Second, results might be influenced by the high rate of coronal devoicing found for Portuguese.

Portuguese has been shown to behave differently than other Romance languages when it comes to voicing patterns [START_REF] Pape | Stop and Fricative Devoicing in European Portuguese, Italian and German[END_REF][START_REF] Hutin | When phonetics meets morphology: Intervocalic voicing within and across words in romance languages[END_REF]. Another explanation could be that in languages which require actual vocal fold vibration to cue the voicing-contrast, such as Romance languages (with the possible exception of Portuguese), speakers might adapt their articulatory strategies to circumvent the AVC and maintain the voicing contrast, as suggested by [START_REF] Solé | Articulatory adjustments in initial voiced stops in spanish, french and english[END_REF]. In other words, the articulatory goal of vocal fold vibration for voiced stops [START_REF] Browman | Articulatory gestures as phonological units[END_REF] is achieved by compensating for the aerodynamic constraints.

Finally, there is also the issue of methodology. The present study relies on language dictionaries with pronunciation variants to detect devoiced stops. When selecting the appropriate variant the ASR system relies on acoustic features, which aren't accessible to us.

Our analysis of the variant selection is mostly phonological.

To thoroughly investigate the relationship between place of articulation and voicing maintenance, as well as language-specific differences within a single language family, large-scale acoustic studies of the data are planned. Acoustic analyses (e.g., voicing probability throughout the stop) would give us a clearer picture of the stop devoicing phenomenon. Furthermore, the present analysis should be extended to languages that, similarly to Romance languages, need vocal fold vibration during the stop's closure (Polish, Bulgarian, Catalan, Dutch) as well as to languages that do not rely on voicing during the stop's closure (German, English, Mandarin).

Figure 1 :

 1 Figure 1: Percentages of devoiced stops detected in the corpora as a function of Language (from left to right: French, Italian, Portuguese, Romanian, Spanish) and Place of Articulation (from left to right: bilabial, coronal, velar).

1 .

 1 PLACE OF ARTICULATION • velar contrast ∼ -1/3 bilabials, -1/3 coronals, 2/3 velars • bilabials vs coronals: -1/2 bilabials, 1/2 coronals 2. POSITION IN WORD • final contrast ∼ -1/3 initial, -1/3 medial, 2/3 final • initial vs medial: -1/2 initial, 1/2 medial 3. VOICING ENVIRONMENT • voicing contrast ∼ -1/3 pause, -1/3 voiceless, 2/3 voiced • pause vs voiceless: -1/2 bilabials, 1/2 coronals 2.3. Predictions

Figure 2 :

 2 Figure 2: Rates of devoiced stops detected in the corpora for each categorical variable in the model.

Table 1

 1 shows the counts of phonemically voiced stops in the corpora for each of the five languages.

		b	d	g	Total
	Fre	25,274 197,543 24,731	247,548
	Ita	34,289 183,571 26,877	244,737
	Por	26,689 119,566 25,125	171,380
	Rom 100,518 398,219 87,399	586,136
	Spa 299,596 658,745 119,196 1,077,537

Table 1 :

 1 Counts of phonemically voiced stops per language: French (Fre), Italian (Ita), Portuguese (Por), Romanian (Rom), Spanish (Spa).

  .

	Language bilabial coronal	velar
	French	6.69%	6.7%	7.01%
	Italian	7.58%	6.11%	8.25%
	Portuguese 8.23% 15.12% 10.03%
	Romanian 6.81%	8.28%	8.43%
	Spanish	7.19%	8.79%	8%

Table 2 :

 2 Percentage of bilabial, coronal and velar devoiced stops per language.

Table 3 :

 3 Rates

of devoiced stops detected in the corpora for each categorical variable in the model.

Speech Corpora and Big Data ID: 429

Speech Corpora and Big Data


Mélanie Cécile Mahiou

Mélanie Heydari

scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.


C A Knudson

Caroline Freissinet
email: caroline.freissinet@latmos.ipsl.fr

H Graham

A C Mcadam

Maëva Millan

Cyril Szopa

D Archer

R V Morris

R Navarro-Gonzalez

J Lasue

Experiments C A Knudson

P D Archer

J Stern

C Malespin

P Mahaffy

The Characterization and Development of a Mineralogical Analog of the MSL Cumberland Drill Sample for Organic Molecule Identification in SAM-Like Experiments

published or not. The documents may come

Introduction:

The Mars Science Laboratory (MSL) Curiosity rover carries a suite of instruments which provide complementary geochemical and mineralogical data to investigate the possibility of habitable conditions throughout martian history [1]. The Cumberland (CB) drill sample from the Sheepbed mudstone in Gale Crate was analyzed by MSL instruments between sols 282-408 [2,3] and the first evidence for an indigenous martian organic molecule, chlorobenzene was made by the Sample Analysis at Mars (SAM) [4] instrument during pyrolysis [2]. A mineralogical analog of the Cumberland sample was created in the laboratory to aid in the understanding of the organic decomposition of precursor molecule(s) that lead to the detection of chlorobenzene.

Although both the carbon and chlorine are of martian origin, it is thought that the chlorobenzene detected on Mars is the result of a reaction between organic precursor(s) and an oxychlorine phase (likely Mg perchlorate [5]), inside the SAM ovens, during the pyrolysis step. Laboratory experiments demonstrate that benzoic acid is a good candidate for a possible precursor of chlorobenzene, as the carboxylic acid functional group would readily substitute for Cl during SAM-like pyrolysis in the presence of Mg perchlorate, forming chlorobenzene. To investigate benzoic acid as a precursor of chlorobenzene in SAM-like conditions, we ran pyrolysis of the CB analog sample on the SAM tested, a high-fidelity mockup of the SAM flight instrument, available at NASA Goddard Space Flight Center.

Analog Preparation: The Cumberland analog (CBA) was created using the minerals and mineral abundances of the Cumberland drill sample as determined by the CheMin instrument [3]. Some minor phases were substituted or combined (e.g. anhydrite was used in place of the minor amount of bassanite and augite was used in place of pigeonite) (Figure 1). Three separate mixtures were created; all three contain the same crystalline minerals but the amorphous component and the clay mineral vary. Results here are from the first mixture, where the amorphous component is a Hawaiian palagonite (HWML101) and the clay mineral is an Fe-smectite, Griffith saponite, collected from Griffith Park in Los Angeles, California. This particular Fe-smectite was used because it is structurally very similar to the trioctahedral clay mineral identified by CheMin [3,6].

2489.pdf 50th Lunar and Planetary Science Conference 2019 (LPI Contrib. No. 2132)

were characterized using X-Ray Diffraction (XRD) and SAM-like Evolved Gas Analysis (EGA) and Gas Chromatograph Mass Spectrometry (GCMS). XRD analyses were carried out on a Bruker D8 Discover Diffractometer (Cu-Kα radiation (λ = 1.54059 Å)). EGA was performed on a Setaram LabSys Evo calorimeter/thermogravimeter coupled to a Pfeiffer Om-niStar Quadrupole Mass Spectrometer (QMS) set up to operate under SAM-like conditions. GCMS analyzes were carried out on a CDS Analytical Pyroprobe 5000 coupled to an Agilent Trace Ultra GCMS and Thermo ISQ LT single QMS. XRD analyses allowed for the purity of the components to be characterized, and the EGA and GCMS analyses provided background data for the decomposition of any trace minerals and organic compounds present in the analog components (e.g. cyclic hydrocarbons, sulfur compounds).

All of the components were individually crushed and sieved to < 150 μm and then mixed by mortar and pestle to create a homogeneous sample. This sample was divided into equal portions which were used for different SAM-like and MSL-like analyses on various instruments. Here, we will focus on the experiments and the results of the samples analyzed on the SAM Testbed at NASA Goddard Space Flight Center.

SAM-like Experiments:

The SAM Testbed is set up to operate at Mars conditions and is an exact replica of the SAM instrument onboard the Curiosity rover [7]. To confirm that benzoic acid could be a precursor of chlorobenzene, the analog was spiked with 0.5 wt. % benzoic acid and 2 wt. % Mg perchlorate. The sample was run on the SAM Testbed using similar conditions (e.g. temperature ramp rate, temperature range sampled to the GC trap during heating) to those which were used for the analysis of the martian Cumberland sample on Mars in which the discovery of martian chlorobenzenes was made [2].

Results: The SAM Testbed results showed the presence of chlorobenzene in the CBA run, at a level similar to the chlorobenzene detection in Cumberland on Mars (~28 pmol) [2]. A subsequent Testbed blank run of inert fused silica spiked with 2 wt. % Mg perchlorate, was run to dismiss the possibility of chlorobenzene formation from the testbed itself in the presence of HCl, and displayed a background chlorobenzene level of only ~2 pmol. This exciting result strongly supports the idea that benzoic acid, or a similarly structured organic molecule, as the most likely organic precursor of the chlorobenzene detected on Mars.

Additional MSL-like Analyses: While the main purpose for the production of this analog was to understand organic evolution using SAM-like instruments on a Mars analog material, sample was sent to other MSL instrument teams (e.g. ChemCam, CheMin) for analysis on those testbed or flight spare instruments as well [8,[START_REF] Lasue | LPSC L. 2489.pdf 50th Lunar and Planetary Science Conference[END_REF]. A portion of the CBA will also be analyzed using a Mars 2020 SuperCam model for comparison to MSL ChemCam results. Additional portions of CBA mixtures are available as a resource to the wider science community in support of MSL data interpretation.

Figure 1 .

 1 Figure 1. The minerals and their abundances of the Cumberland (CB) sample analyzed on Mars (A) [3, 8], and the laboratory-made Cumberland analog (CBA) (B).

E