Q Boyer

D V A Nguyen

P Rougeot

O Gaiffe

B Tamadazte

J Szewczyk

L Tavernier

Y Nguyen

K Rabenorosoa

OCT as tool for laser ablation monitoring applied to cholesteatoma

I. INTRODUCTION

Cholesteatoma is an accumulation of exfoliated keratin from squamous epithelium that invades the middle ear, erodes the bony structures, and causes hearing loss as well as other serious complications. The only treatment of this disease is surgical intervention. As the middle ear cavity is small and contains hidden recesses, the complete removal of the disease is still challenging and the recurrence rate can reach 25% for conventional methods. An advanced treatment for this disease is laser surgery that has been proven to remove efficiently the residual cholesteatoma, thus reducing the recurrence rate [START_REF] Nobel | Recommendations for Potassium-Titanyl-Phosphate Laser in the Treatment of Cholesteatoma[END_REF]. In the µRoCS project1, we aims to propose a dexterous continuum robot with embedded laser instrument for exhaustive cholesteatoma removal. As part of this project, this study focuses on measuring the vaporized volume of cholesteatoma during the laser ablation process depending on the laser parameters. This volume will be obtained using OCT scanning and the proposed image processing. The result has an important role to vaporize the right amount of cholesteatoma tissue as insufficient removal would result in cholesteatoma recurrence and excessive removal would damage the healthy structures nearby.

II. MATERIALS AND METHODS

A. Experimental setup

An LBO fibered laser (Velas-8G, 532 nm, 8 W) was employed to vaporize cholesteatoma samples collected from Besançon Hospital2 (see Fig. 1). The optical fiber has a core diameter of 400 µm and a numerical aperture of 0.22. The studied parameters are the laser power (6 W and 8 W) and the exposure time (100 ms to 250 ms). The distance between the fiber tip and the tissue is important due to the divergence of the exiting laser beam. It should be as short as possible so the size of the laser spot is minimal. A small laser spot allows for precise ablation while limiting the unwanted heating of surrounding tissues. The fiber tip * Q. Boyer and D. V. A. Nguyen contributed equally to this work.

1https://anr.fr/Project-ANR-17-CE19-0005 2The samples used are human surgical wastes collected under ethical approval. is manually positioned in contact with the sample before each laser pulse. The distance is therefore considered constant throughout the study. Then, the tested laser pulse was shot to vaporize a small amount of cholesteatoma tissue. To estimate this vaporized volume, 3d-images (Cscan) of the sample were acquired using a TELESTO-II OCT with an LSM03 probe. The voxel dimensions were 25 µm along the X and Y axes and 3.5 µm along the Z axis (optical axis). Eventually, a UR3 collaborative robot arm was used to sequentially position the sample under the laser instrument and the OCT.

B. Cholesteatoma segmentation

The main phases of the cholesteatoma segmentation are presented in Fig. 2. First, a threshold is applied to roughly separate the volumes of matter (cholesteatoma and support) from the background (Fig. 2b). In order to remove the support, we extract from the obtained image two arbitrary cross-sections parallel to the (0𝑥𝑧) plane that only contain the support element, not the tissue (e.g., at the boundaries of the image). The outlines of these crosssections are then obtained using the Canny edge detector (edge function in Matlab). As the support has a prismatic shape, it's surface can be approximated by performing a linear interpolation between the outlines detected in the previous step. This can then be used to subtract the support from the binary image obtained after thresholding. At this stage, the image may still contains noises: either false negatives (holes in the middle of the matter) or false positives (matter in the background) (Fig. 2c). The imfill function in Matlab is used to remove the false negatives. It detects and fills the holes characterized by background pixels that cannot be accessed from the boundary pixels of the image through the face connectivities (6-connected mode). false positives are filtered using the opening morphological operation (imopen function in Matlab) with a structuring element based on a 50 µm-radius ball. The solid component corresponding to the cholesteatoma is then extracted using the bwselect3 function of Matlab (Fig. 2d). Its volume can be simply estimated by counting the voxels and multiplying the result by the voxel element volume. The computation time for the segmentation is about 5 s for a volume of 171 × 193 × 768 voxels using a 1.6 GHz Intel Core i5.

C. Vaporized volume of cholesteatoma

The vaporized volume can be calculated as the difference of volume in the images before and after the laser ablation.

The experiments were conducted in the hours following the cholesteatoma removal. Positioning the sample under the fiber tip takes around 1 minute and the scanning time is 20 seconds so each iteration takes a few minutes. The time delay between the operation and the experiments should be kept as low as possible because time alters the optical properties of the cholesteatoma [START_REF] Wisotzky | Determination of optical properties of cholesteatoma in the spectral range of 250 to 800 nm[END_REF].

III. RESULTS

The experimental results of the vaporized volumes with respect to variant time exposures of the laser pulse for two different laser powers, 6 W and 8 W, are presented in Figure 3. In this experiment, the OCT can measure an ablation volume of 0.03 mm 3 (its resolution is even about 2.2 × 10 -6 mm 3 for each voxel volume). One can confirm that the vaporized volume increases linearly with the exposure time. Moreover, the steady-state ablation rate increases when the laser power increases. This experimental result is consistent with the computational model in the literature [START_REF] El-Khalil | A threedimensional transient computational study of 532-nm laser thermal ablation in a geometrical model representing prostate tissue[END_REF]. As the tissue needs to be heated from its initial temperature (20 °C) to the water vaporization temperature (100 °C), there exists a minimum value of the pulse duration so that cholesteatoma can be vaporized with a given laser power. This limited value can be estimated using linear extrapolation from the vaporized volumepulse duration line (e.g., 6 W and 105 ms).

IV. CONCLUSIONS AND DISCUSSION

This study provide an effective method for cholesteatoma laser ablation monitoring using OCT and the proposed image processing. Based on the obtained quantitative results, the surgeon can select the proper laser parameters (power and time exposure) as well as the required number of shots for any specific volume of residual cholesteatoma during the laser ablation process. More experiments can be conducted to obtain a complete description of the relationship between the cholesteatoma vaporized volume and the laser parameters.

As an ex-vivo study, there are probably several differences in the results comparing to the in-vivo practice such as: the presence of blood in the tissue, the higer temperature in human body (37 °C), or the tissue shrinkage due to natural evaporation of water in ex-vivo case. The best solutions to these limitations are making the experimental environment close to the middle ear condition and avoiding intraoperative bleeding. Future work will focus on the integration of the OCT probe into our hybrid concentric tube robot [START_REF] Nguyen | A hybrid concentric tube robot for cholesteatoma laser surgery[END_REF] to perform cholesteatoma detection and laser ablation monitoring. The integrated OCT probe will also be used to control the distance between the fiber tip and the cholesteatoma during the ablation and study its impact on the ablation process.

Fig. 1 :

 1 Fig. 1: (a) Experimental Setup. (b) Cholesteatoma before and (c) after a laser ablation.

Fig. 2 :

 2 Fig. 2: Steps of cholesteatoma OCT image processing.

Fig. 3 :

 3 Fig. 3: Vaporized volume as a function of exposure time.


Houda Hachem

Ben Sassi

Nasrallah

EXPERIMENTAL STUDY OF THE OPERATION CONDITIONS OF STABILITY ON A GAMMA STIRLING ENGINE

Keywords: Stirling engine, Disruption influence, Performances variations, Stability conditions, Relaxation time

Considering Stirling engines modern applications and cogeneration recovery energy from industrial process, the power of a Stirling prime mover is to be provided at a speed of rotation adapted to the operation of the receiver system (usually a generator) to exploit the performance of this machine under the conditions of its use (ie lowering of the rotational speed and torque transmitted rise or, more rarely, elevated speed and lowering the torque transmitted). Knowing that the hot air engine cannot change speed quickly and in order to have a well designed system, it is important to study the unsteady state conditions. In this work we present an experimental stability analysis of an irreversible heat engine working at different conditions. The experimental study aims at analyzing the effect of working parameters disruption on the stability of the Gamma Stirling engine. Parameters involved in this experimental study are the load pressure of the motor and the load applied to the Stirling engine. The influence of engine operating parameters on its torque and rotational speed is investigated. The time required by a gamma type Stirling engine to stabilize operation after disruption is estimated. Results show that after a small disruption, speed and temperature evolutions decays exponentially to the steady state determined by a relaxation time. It is assumed that the decrease of the applied power load to the engine or the increase of the load pressure leads to a speed up. And that the increase of the applied power load to the engine or the decrease of the load pressure leads to a speed down.

INTRODUCTION

Major studies in literature of Stirling engine focused on steady state energetic proprieties. Only few researches aim at analyzing the stability of the engine numerically. However, the stability analyses described in literature are not validated experimentally. Numerically, Ladino-Luna et al.

[1] investigated local stability of thermal engines modeled as an endoreversible Curzon and Ahlborn cycle when a non-linear heat transfer law is assumed. Huang et al. [START_REF] Huang | Local stability characteristics of a non-endoreversible heat engine working in the optimum region[END_REF] studied the local stability of a nonendoreversible heat engine working between the maximum power output and the maximum efficiency is analyzed based on stability criteria for almost linear systems. They defined an internal irreversibility factor I. When I=1, the engine is endoreversible. When 0<I<1, the engine is internally irreversible. Reyes-Ramírez et al. [START_REF] Reyes-Ramírez | Global Stability Analysis of a Curzon-Ahlborn Heat Engine under Different Regimes of Performance[END_REF] presents a global stability analysis of a Curzon-Ahlborn heat engine considering different regimes of performance: maximum power (MP), efficient power (EP) and the so-called ecological function (EF). Sanchez-Salas et al. [START_REF] Sanchez-Salas | On the Dynamic Robustness of a Non-Endoreversible Engine Working in Different Operation Regimes[END_REF] extended a previous work and studied the local stability of an endoreversible Curzon-Ahlborn engine working in the ecological regime, with inclusion of the engine inherent time delays to account for internal irreversibilities. Besides, in order to compare the ecological and maximum power regimes, they included a third criteria named Maximum efficient power. Barranco-Jimenez et al. [START_REF] Barranco-Jimenez | Local Stability Analysis of a Thermo-Economic Model of a Chambadal-Novikov-Curzon-Ahlborn Heat Engine[END_REF] showed that the relaxation times are function of the temperature ratio 21 / TT   with T2>T1, the cost function f and the parameter R (a parameter related to the degree of internal irreversibilities). They observe that the stability of the system improves as τ increases whereas for changes in f and R. Barranco-Jimenez et al. [START_REF] Barranco-Jimenez | Finite-Time Thermoeconomic Optimization of a Solar-Driven Heat Engine Model[END_REF] carried out a thermoeconomic optimization of an irreversible solar-driven heat engine model by using finitetime/finite-size thermodynamic theory. They take into account losses due to heat transfer across finite time temperature differences, heat leakage between thermal reservoirs and internal irreversibilities in terms of a parameter which comes from the Clausius inequality.

Any thermal engine is exposed to continuous internal and external perturbation while operating. This experimental study measure the time required by a Gamma Stirling engine to operate on a stable operation speed changes during the intervention of different parameters to its initial stable condition. The parameters involved in this experimental study are the load pressure of the motor and the load applied to the Stirling engine. This experimental study aims to determine the influence of engine operating parameters on its torque and rotational speed. The time required by a gamma type Stirling engine to stabilize operation avec perturbation will be estimated.

NOMENCLATURE

P1 : Pressure before disruption P2 : Pressure after disruption ΔN : speed variation P load : Applied power load to the engine C 1 : torque before disruption C 2 : torque after disruption Pu 1 : output power before disruption Pu 2 : output power after disruption N 1 : rotational speed before disruption N 2 : rotational speed after disruption

EXPERIMENTAL FACILITY AND METHODOLOGY

A Gamma type Stirling engine will be investigated in this study. It uses air as working fluid and can deliver a maximum output mechanical power of about 500 W. Its maximum rotation speed is around 600 rpm when the maximum working pressure is about 10 bar. This engine is mainly composed of a compression cylinder (C), an expansion cylinder (E) and three heat exchangers (heater, regenerator, cooler). The regenerator (R) is a porous medium (i.e., made of stainless steel with 85% porosity). In the real engine, the cooler (K) acts as a cold source heat exchanger formed by an open water circuit and the heater consists of 20 tubes in order to increase the exchange surface between the working gas and the hot source (H). In the modelling assumptions, the hot source of the Stirling engine is the expansion cylinder wall and the cold source is the compression cylinder wall (cf. Figure 1a). The geometric properties of this Stirling engine are presented in previous studies of Gheith et al. [START_REF] Gheith | Experimental investigations of a Gamma Stirling engine[END_REF][START_REF] Gheith | Study of regenerator constituting material influence on a Gamma type Stirling engine[END_REF][START_REF] Gheith | Determination of adequate regenerator for a Gammatype Stirling engine[END_REF][START_REF] Gheith | Optimization of Stirling engine performance based on an experimental design approach[END_REF], with some experimental investigations. In particular, the influence of initial filling pressure, engine speed, cooling water flow rates and heating temperature on the engine performances (brake power and efficiency) has been highlighted. More recently, Hachem et al. [START_REF] Hachem | Global numerical characterization of a γ-Stirling engine considering losses and interaction between functioning parameters[END_REF] presented a global energetic modeling of the same Stirling engine. They showed that the engine rotation speed have an optimum value that guarantees the best Stirling engine performances. For low rotation speeds, the brake power increased with rotation speed, whereas for high rotation speeds, the brake power decreases when the rotation speed increases. The increase of initial filling pressure leads to an increase of working fluid mass. The increase of hot end temperature leads to an increase of the thermal exchanged energy. These two phenomena cause the increase of the engine brake power. The authors demonstrated that engine brake power is also sensitive to its heat exchanger efficiency. The engine regenerator is the most influencing component. Several type-K thermocouples with a diameter of 0.5, 0.25, and 0.0254mm were used to measure the temperature at different locations of the engine. Because the interest was to measure the instantaneous temperatures, proper care has been taken regarding the size and the thermal inertia (which should be very low) of the thermocouples. Two thermocouples were mounted upstream of the expansion and compression spaces, respectively. Other two thermocouples were used to measure the inlet and outlet temperatures of the water supplying to the cooler. Eight thermocouples are implemented to measure the temperature of the working gas passing through the symmetrical side of the regenerator matrix (figure 2). The thermocouples were skinned up to 1mm in the regenerator matrix without touching the material. The Stirling engine drives an alternator (figure1.a) which feeds a load consisting of a series of lamps 10 (figure1.b) connected in parallel to each electric resistance 55W. The first experimental test is to switch on lamps even to accelerate or to decelerate the engine. The second experimental test is to change immediately the filling pressure of the engine using an external compressor (figure1.c) allowing a constant pressure inside the engine. The two different tests aim at describing the temporal evolution of temperature and speed excitement after a disruption of the operating steady state to analyze the effect of an operating parameter brusque variation on the stability of the operating steady state of the Stirling engine and determine the time required to regain steady state is calculated in each experiment. The first disruption is the dissipated load at the output of the engine and the second disruption is the filling pressure.

Disruption 1: Dissipated load at the output of the engine

In this section, we study the response of the machine to the power required by the alternator. we discuss the operation of the Stirling engine for different braking power applied to the piston. Initially the engine is working at an initial constant steady state regime for which the torque is C 1 , the output power is Pu 1 , the rotation speed is N 1 and no dissipated load power is connected to the alternator. Then a sudden change of the dissipated load power is done when switching on load lamps. Experimental tests are described in table 1. Cases from 1 to 5 in tables 1 and 2 are a speed up tests and cases 6, 7 and 8 are slow down tests. Evolutions of (figure 3 and6) and temperatures (figure 4 and 5) are recorded until attending the final steady state of the system. Initially the engine presents the same behavior for the five different cases. During the starting of the disruption, the engine is forced to speed up and all the functioning parameters such as pressure, temperatures and speed follows an exponential evolution until the new steady state is reached. The stability of the system improves as the intensity of the brusque disruption decreases. The relaxation time depends on the intensity of the brusque disruption.

From figure 3, we can understand that the relaxation time increased slowly as the intensity of the brusque disruption increases this implies that the reduction of the applied power load to the engine improve the system stability.

From figures 4 and 5, it is seen that Engine acceleration results in an increase of the temperature inside the compression cylinder (cold side) and a decrease of the temperature inside the expansion cylinder (hot side).

From table 2, it is seen that the produced torque of the Stirling engine decreases after a speed up and increases after a speed down of the engine. When increasing the engine rotation speed, its performances are deteriorated (decrease of its total efficiency and the produced power of the engine) because of increased friction losses. In case 5, the engine speed increases from the initial angular velocity of 431 rpm to 523rpm in 140s. compared to the traditional Stirling engine, the present engine takes longer time to reach the steady state regime. This is because leakage losses in the engine.

Start of disruption

Before disruption

After disruption

Before disruption

After disruption intensity of the filling pressure disruption. It is clearly seen (figure 7) that once the disruption begins, speed increases immediately from 350rpm to 420rpm in case 4. But it decreases due to irriversibilities inside the engine and finally regains its peak value about 420rpm. The transient state is characterized by two phases. it can be observed that the speed value in the final steady state periode is equal to its pic value at disruption (figure 7). During the first phase the temperature decreases due to the increase of filling pressure which means an increase of the filling mass of air inside engine. During the second phase, which lasts longer than the first phase, the introduced cold mass of air takes a lot of time to warm up. Thermocouples TR1 and TR5 are closer to the disturbance that is why their intensity of variation are higher than the temperature variation recorded by thermocouplesTR4 and TR8 (figure 9). Having regard to the introduction of a cold mass of air into the engine, its speed decreases over time (phase 1). Once the temperature of the introduced air increases, the engine speed increases gradually (phase 2) until reaching the steady state regime. After a small perturbation, the system decays exponentially to the steady state characterized with the relaxation time. At a sudden decrease of engine's filling pressure: the rotation speed of the engine decreases immediately and risks packing the shaft work of the engine. Then return to each initial value and decay exponentially until reaching the final steady state speed value. In case 4, speed decays from about 390rpm to 318 rpm (figure 9). When lowering of the rotational speed, temperature evolution inside regenerator has inverse its evolution when increasing the engine (figure 10). Directly after the starting of the disruption, a great difference between cases is clearly seen, but once the final steady stade is reached, these differences are reduced as fig 9-10 and 11 illustrates.

CONCLUSION

The Stirling engine is exposed to continuous internal and external perturbations while operating. But the hot air engine cannot change speed quickly. This analysis guarantees an experimental dynamic behavior of the engine like stability and relaxation time. The stability of the system improves as the intensity of the brusque disruption decreases. The relaxation time depends on the intensity of the brusque disruption. Not only working parameters disruption affects the engine's stability, geometrical characteristics of each part and physical proprieties of the working gas used in the engine act on its possibility to change its regime from a steady state to another.

FIGURE 1 .FIGURE 2 .

 12 FIGURE 1. EXPERIMENTAL SETUP OF THE GAMMA STIRLING ENGINE

FIGURE 9 .FIGURE 10 .

 910 FIGURE 9. TEMPORAL EVOLUTION OF SPEED DOWNWHEN CHANGING THE FILLING PRESSURE

TABLE 1 .

 1 DESCRIPTION OF MAIN TEST BY CHANGING THE DISSIPATED LOAD POWER

	Case ΔN(rpm)	P load (W)	Description
	1	7.4	75	Speed up
	2	52.3	150	Speed up
	3	81.8	225	Speed up
	4	84.2	300	Speed up
	5	92	375	Speed up
	6	-75.6	0	Speed down
	7	-82.1	0	Speed down
	8	-90.9	0	Speed down

TABLE 2 .

 2 EXPERIMENTAL TESTS WHEN CHANGING THE DISSIPATED LOAD POWER OF THE ENGINE

		C1	C2	Pu1	Pu2	N1	N2
	Case	(Nm)	(Nm)	(W)	(W)	(rpm)	(rpm)
	1	3.83	3.47	168.20 158.35 434.5	441.9
	2	3.95	3.34	173.22 164.68 428.7	481
	3	4.08	3.36	177.61 175.50 426.3	508.1
	4	4.04	3.49	178.75 183.19 430.2	514.4
	5	4.05	3.45	177.82 185.26	431	523
	6	3.76	4.08	166.35 173.49 518.9	428
	7	3.34	4.04	174.29 176.75 510.1	428
	8	3.42	4.08	179.43 178.79 503.6	428

applied to the initial steady state takes about 16 minutes before attending the final steady state. The intensity of perturbation didn't change the relaxation time needed to reach the final steady state. But the speed increases even when increasing the

ACKNOWLEDGMENTS

This work was supported by the laboratory LAMIH CNRS UMR 8201 (University of Valenciennes), the laboratory LESTE (ENIM, Monastir, Tunisia) and the European Commission within the International Research Staff Exchange Scheme (IRSES) in the 7th Framework Programme FP7/2014-2017/ under REA grant agreement n°612230. These supports are gratefully acknowledged.


Sebastião Quintas

Julie Mauclair

Virginie Woisard

Julien Pinquier
email: julien.pinquier@irit.frwoisard.v@chu-toulouse.fr

Automatic Assessment of Speech Intelligibility using Consonant Similarity for Head and Neck Cancer

Keywords: speech intelligibility, pathological speech, automatic speech processing, head and neck cancer

The automatic prediction of speech intelligibility is a widely known problem in the context of pathological speech. It has been seen as a growing and viable alternative to perceptual evaluation, which is typically time-consuming, highly subjective and strongly biased. Due to this, the development of automatic systems that are able to output not only unbiased predictions, but also interpretable scores become relevant. In this paper we investigate a method to predict speech intelligibility based on consonant phonetic similarity. The proposed methodology relies on a siamese network to compute similarity scores between healthy and pathological phonemes, and based on the combination of those scores, regresses the intelligibility values. Our experimental evaluation suggests a high baseline correlation value of p = 0.82, when applied to our corpus of head and neck cancer. Moreover, further conditioning of the system on specific phonemes in key contexts increased the correlation up to p = 0.89. The given methodology also aims to promote interpretability of the predicted intelligibility score, which is highly relevant in a clinical setting.

Introduction

Head and neck cancer (HNC) is a type of cancer with major functional repercussions on breathing, swallowing and speech. Due to this, a communication impairment is likely to appear, impacting the speech-related quality of life. As a result, perceptual evaluation has long been the most used method for therapists to assess disordered speech. On the other hand, perceptual evaluations are very time-consuming, biased and variant, since the evaluation can be conditioned on, for example, patients previously assessed by the same therapist [START_REF] Balaguer | Effects of oral and oropharyngeal cancer on speech intelligibility using acoustic analysis: Systematic review[END_REF]. Due to the biased nature and low reproducibility of these scores, and also due to the increasing rate of oropharyngeal cancer incidence, the development of an automatic assessment that is able to output unbiased and reproducible intelligibility measures becomes of high interest [START_REF] Middag | Automatic analysis of pathological speech[END_REF][START_REF] Fex | Perceptual evaluation[END_REF].

From the literature, one can distinguish different ways to predict speech intelligibility for pathological speech. These methods can range from approaches such as regressing a score from the word error rate achieved by automatic speech recognition (ASR) systems [START_REF] Christensen | A comparative study of adaptive, automatic recognition of disordered speech[END_REF], to extract relevant features from pathological speech, using automatic speech processing technologies [START_REF] Middag | Automated intelligibility assessment of pathological speech using phonological features[END_REF][START_REF] Fredouille | Acoustic-phonetic decoding for speech intelligibility evaluation in the context of head and neck cancers[END_REF]. Speaker embeddings, such as i-vectors or x-vectors, have also proved to be a viable alternative for intelligibility estimation [START_REF] Quintas | Automatic prediction of speech intelligibility based on x-vectors in the context of head and neck cancer[END_REF][START_REF] Laaridh | Automatic evaluation of speech intelligibility based on i-vectors in the context of head and neck cancers[END_REF]. Similarity estimation systems, such as siamese networks, have seen a growing use in tasks such as speaker verification [START_REF] Wan | Generalized end-to-end loss for speaker verification[END_REF] and sentence similarity [START_REF] Mueller | Siamese recurrent architectures for learning sentence similarity[END_REF]. Recent works, such as [START_REF] Wang | Child speech disorder detection with siamese recurrent network using speech attribute features[END_REF] and [START_REF] Ng | Automatic detection of phonological errors in child speech using siamese recurrent autoencoder[END_REF], used the aforementioned methodology in a pathological speech context. In both cases, the systems were developed for the detection of children's speech disorder, focusing on the binary task of detecting specific mispronunciations.

In the context of pathological speech, besides being unbiased and reproducible, it is also highly relevant that an automatic approach maintains explainability of the produced estimations, which typically lacks in the automatic systems based on deep learning. An explainable system could provide more relevant cues in a clinical setting and promote more objective measures. The interpretability of the results, that normally lacks in machine learning systems, can also be used to build trust in the implementation of automatic approaches [START_REF] Diprose | Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator[END_REF].

In the present work, we introduce an intelligibility prediction system based on consonant similarity. There are multiple motivations behind the development of such system, namely that: (i) ASR based intelligibility prediction systems typically underperform in patients with severe speech impairments [START_REF] Christensen | A comparative study of adaptive, automatic recognition of disordered speech[END_REF]; (ii) Individual phonemes, especially consonants, are highly relevant for perceptual speech intelligibility [START_REF] Nuffelen | Speech technology-based assessment of phoneme intelligibility in dysarthria[END_REF][START_REF] Saravanan | Speech outcome in oral cancer patients -pre-and post-operative evaluation: A cross-sectional study[END_REF], either healthy [START_REF] Fort | Consonants are more important than vowels in the bouba-kiki effect[END_REF] or pathological [START_REF] Crevier-Buchman | Intelligibility of french consonants after partial supra-cricoid laryngectomy[END_REF]; (iii) Automatic systems tend to lack explainability [START_REF] Mccoy | Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based[END_REF], which is normally demanded by health practitioners [START_REF] Diprose | Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator[END_REF]. Given this, we propose an automatic system that predicts speech intelligibility based on consonant similarity, that is able to output not only an objective, but also a fully explainable prediction. Since that in our previous work, we found that there are sentences able to conduct a more accurate intelligibility prediction [START_REF] Quintas | Automatic prediction of speech intelligibility based on x-vectors in the context of head and neck cancer[END_REF], in the present work we also explore the relevance of specific phonemes in our automatic speech intelligibility score.

The rest of this paper is organized as follows. Section 2 introduces our proposed system and the function used to regress our intelligibility values. Section 3 details our experiments on the French HNC Speech Corpus (C2SI). Section 4 presents an analysis of the obtained results, the post-treatment and the discussion. Finally, section 5 summarizes the results of the work.

Methodology

The proposed methodology relies on three steps. The first one corresponds to the feature extraction and data preparation. The second uses a recurrent siamese network in order to compute the phonetic similarity between two phones. Finally, the third step is the computation of the intelligibility score based on the phonetic similarity scores previously achieved.

Data Preparation

We used individual phones as input to our system, which were obtained via forced alignment using the Montreal Forced Aligner toolkit [START_REF] Mcauliffe | Montreal forced aligner: Trainable text-speech alignment using kaldi[END_REF]. For each file, 13 Mel Frequency Cepstral Coefficients (MFCCs) were extracted. Filterbank features, MFCCs + delta coefficients and phonetic posteriors [START_REF] Vasquez-Correa | Phonet: a tool based on gated recurrent neural networks to extract phonological posteriors from speech[END_REF] were also experimented, however, in our specific context they conducted to a poorer generalization ability of the system. Interestingly, the work of [START_REF] Lin | Classifying speech intelligibility levels of children in two continuous speech styles[END_REF] also found that simple MFCCs conducted to a better intelligibility classification in continuos children's speech, when used in a recurrent system. The phones were then paired in two categories: same-phoneme and different-phoneme pairs, which were used to train the network. Due to the nature of the corpus used, further explained in subsection 3.1, and the motivation stated in section 1, the phonemes used in this study correspond to the 16 French consonants.

Siamese Network

The proposed system uses a siamese network to detect phonetic similarities. The system receives as input two phones, one as a reference and another as a test, and computes their similarity. A mispronunciation is detected whenever the test phone is found to be dissimilar from the reference one. The system uses two bilateral Gated Recurrent Units (GRU) as encoders with shared weights, in which the two outputs form embedding representations (see figure 1). The encoders are comprised of two hidden layers, with a hidden dimension of 100. The embedding representation corresponds to the concatenation of the last two hidden states, of forward and backward context of the bilateral GRU used. This generates an embedding representation of size 200. The absolute difference between the two fixed-length representations is then computed. It is expected that the system models the input phonemes so that same-pair phonemes are closer in the embedding space than different-pair phonemes. Afterwards, a Deep-Neural-Network block is appended. This block is composed of 3 fully-connected layers of size 200. The Rectified Linear Unit function is used as an activation function in all of the layers except for the final one, which used a sigmoid. A dropout rate of 0.25 and batch normalization are applied on every fullyconnected layer.

Intelligibility Estimation

Based on the similarity scores obtained by our system, we compute an intelligibility score for each speaker:

I(Sa) = 16 n=1 Sima(n) Tota(n) 16 * 10 (1)
This scoring function corresponds to the arithmetic mean of each patient's individual consonant score. Sima(n) refers to the number of similar phones of one of the 16 consonants obtained by a speaker (Sa), while Tota(n) is the total number of representations of that consonant issued by a speaker.

Experiments and Results

C2SI Corpus

The present work made use of the French head and neck cancer speech corpus C2SI [START_REF] Woisard | C2SI corpus: a database of speech disorder productions to assess intelligibility and quality of life in head and neck cancers[END_REF]. The corpus includes a variety of patients that suffer from the oral cavity or oropharyngeal cancer, with different onset tumor locations, and also healthy speakers. All of the patients were asked to record a variety of tasks, and in this study, we used the isolated pseudo-word task. In this task, each speaker was asked to record a set of 52 pseudo-words, nonexistent in the French language [START_REF] Lalain | Design and development of a speech intelligibility test based on pseudowords in french: Why and how?[END_REF]. Each pseudo-word was automatically generated so that it respects French phonotactic and orthographic rules, following a C(C)1V1C(C)2V2 structure, where C(C)i is either a single consonant or a consonant group and Vi a single vowel. Each speaker was attributed a phonetically balanced pseudo-word set that contains at least one instance of each consonant in the applicable contexts of beginning and middle of the word. The correct pronunciation of each pseudo-word was played to each speaker before recording. All audio files were recorded with a sampling rate of 48 kHz. While all the french consonants were present in each set of 52 pseudowords, either isolated or in consonant groups, there were only 8 vowel representations. Due to this and the other reasons aforementioned in section 1, we focused mainly on consonant similarity in this study. The intelligibility values, used as targets, were computed based on the independent perceptual evaluation of six different therapists. A score between 0 and 10 was attributed, based on those evaluations, the smaller the score is, the less intelligible the speaker is. A total of 102 speakers, 24 healthy controls (HC) and 78 patients, were used in the present study.

Experimental Setups

In order to learn phonetic similarity, we used same-phoneme pairs as positive and different-phoneme pairs as negative. 24 HC of the C2SI corpus were used as training data. After force aligning and extracting the features, we obtained a total of 3,323 training phones (consonants only). For each one of the 16 consonants groups, an individual training set was created. For a specific training set, every consonant was paired with every other representation of the same consonant, and also to a random set of 650 different consonants. Out of those formed pairs, a random subset of 50k pairs was extracted. This subset corresponds to the final training set for that same consonant.

System Training

Sixteen different models, one for each consonant, were created. Each individual model was fine-tuned on four hyperparameters: learning rate (between 0.001 and 0.0001), epochs (between 2 and 8), batch size (either 256 or 512) and GRU dropout rate (either 0.0 or 0.25). A binary cross-entropy loss function was used, optimized by the SGD algorithm [START_REF] Ketkar | Stochastic gradient descent. in: Deep learning with python[END_REF].

System Validation

A set of six patients from the C2SI corpus, unseen during training, was used to validate the proposed model instead of a subset of the HC. The chosen patients had high intelligibility (over 9.8 on a 0 to 10 scale), making them virtually indistinguishable from HC. The validation phones were grouped into the 16 different consonant groups. Each new phone is paired with all the reference phones seen during training. The median similarity is computed between each test and reference consonant groups (see figure 2). A reliable distinction between all phonetic groups was obtained. This aspect was less evident in the plosive consonants, where the difference between the key consonant to be tested and the other consonants was more subtle when compared to the remaining groups. This was expected due to the nature of forced-alignment, that segments plosives in short time intervals. This in turn provided less contextual information, making the phonetic distinction a slightly more difficult task. This aspect, however, did not prove to be problematic as all speakers were submitted to the same forced-alignment.

Evaluation Scores

To generalize the trained models on the remaining patients of the corpus, we used as a threshold the median values obtained by the validation patients for each consonant group, also known as the diagonal values of figure 2. In order to make the system more robust to the different median values, we added to the threshold the median absolute deviation (MAD) [START_REF] Pham-Gia | The mean and median absolute deviations[END_REF]. The classification method is as follows. If a new phone has a similarity score above the threshold ( xi -M ADi), where xi is the median, it is considered a similar phone, otherwise it is considered dissimilar. The intelligibility score is computed according to equation 1.

By correlating the predicted scores with the given intelligibility reference values, we were able to achieve a Spearman's correlation coefficient of ρ = 0.82 (see figure 3). Figure 3: Intelligibility prediction plot using the proposed consonant similarity approach.

Outlier Analysis

We performed an analysis on the predicted values that had a large deviation from the target intelligibility, all of which had a prediction below target. From the perceptual evaluation of these outliers, we found that the system was underperforming in specific contexts, namely very breathy/hoarse speech and patients with nasalized plosives. All of the cases corresponded to phonetic mispronunciations, and it was expected that the system would classify them as such, since it was neither trained nor validated with speakers that had similar mispronunciations. On the other hand, despite the phonemes being mispronounced, we noticed that in these cases the reference intelligibility values were still high, pointing out that in the aforementioned contexts, those specific mispronunciations had a little affect on the reference intelligibility values.

Phonetic Suppression Post-Processing

We noticed that by suppressing specific consonants we were able to obtain a better intelligibility estimation. Thus, we made use of the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) [START_REF] Eyben | The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing[END_REF]: an ensemble of acoustic parameters tailored for indexing physiological changes in voice production, with an added degree of theoretical significance and explainability, and we went to search for hand-crafted features used in the literature to address the key types of speech impairment aforementioned. We found a set of three acoustic parameters:

• Slope UV0-500 (mean) -Mean value of the linear regression slope of the logarithmic power spectrum within 0-500Hz on unvoiced segments. Related to breathy and hoarse voice qualities [START_REF] Alipour | Measures of spectral slope using an excised larynx model[END_REF].

• Loudness (percentile 20) -Estimate of perceived signal intensity from an auditory spectrum. In our context, we hypothesize that it can help to detect nasalized plosives [START_REF] Tjaden | Rate and loudness manipulations in dysarthria[END_REF] due to the added intensity found in this type of mispronunciation.

• LogRel F0-H1-A3 (mean) -Ratio of energy of the first F 0 harmonic (H1) to the energy of the highest harmonic in the third formant range (A3). Relevant feature for breathy/hoarse voice assessment as well [START_REF] Narasimhan | Spectral measures of hoarseness in persons with hyperfunctional voice disorder[END_REF].

By using this set of features, we can model the speakers with the aforementioned speech pathologies and suppress certain phonetic groups accordingly. The modelization of these speakers according to these features can be found described in table 2, where a threshold indicates the cutoff region for each feature. Results from this phonetic suppression are in table 3.

Table 2: Phonemes used and suppressed in the intelligibility score function according to the GeMAPS features.

Feature name

Suppressed phonemes

Threshold

Used phonemes Slope UV0-500 (mean)

[b], [d], [g], [z], [Z], [v], [m], [n], [l] > 2.41 [p], [t], [k], [s], [S], [f ], [R] LogRel F0-H1-A3 (mean)
< 15.00

Loudness (percentile 20) [p], [t], [k], [b], [d], [g] > 0.31 [s], [S], [z], [Z], [f ], [v] [m], [n], [l],
[R]

Discussion

The results suggest that we can reliably predict speech intelligibility using consonant similarity. Moreover, by conditioning the used consonants on key mispronunciations and the external GeMAPS features, we are able to obtain an even higher level of correlation (ρ = 0.89). This aspect points out that, depending on the speech impairment a speaker may have, there are mispronounced phonemes that do a little contribution to the overall intelligibility score: for patients with a high level of hoarseness, all voiced phonemes were classified as non-similar by the system. By suppressing the voiced phonemes, we were able to obtain more accurate predictions. To detect this hoarseness, we used the Slope and LogRel (see table 2). Any patient that had a feature value above a threshold had their voiced phonemes suppressed, and the remaining phonemes were used for the score. The suppression of the full plosive group, displayed on table 2, also lead to more accurate predictions, showing that those mispronunciations did not affect much the perceptual intelligibility estimations (see correlation values on table 3). As expected, the used features also isolated a few patients that did not have the specific mispronunciations aforementioned, however, the same phonetic suppression poorly affected those intelligibility scores, confirming a certain level of robustness of the chosen features.

The assumption that different phonemes have different degrees of relevance corroborates the fact that for each speaker, there are sentences that are able to convey a better intelligibility estimation than others, concluded in [START_REF] Quintas | Automatic prediction of speech intelligibility based on x-vectors in the context of head and neck cancer[END_REF]. A deeper feature analysis should be investigated in order to identify other contextual key phonemes that are less important in the intelligibility score. Further robust feature conditioning could help provide more accurate scores and also a more objective and explainable patient-specific information. We hypothesize that an a priori knowledge of patient's specific features (e.g. tumor location, reconstruction type), which are known in a clinical context, could condition the system to obtain even more accurate results. We leave this analysis for future work.

The results were also compared to two previous approaches used on the same corpus (see table 3), one based on the application of a Wagner-Fischer algorithm to the distance between the automatically transcribed (ASR) and ground truth pseudo-word [START_REF] Fredouille | Acoustic-phonetic decoding for speech intelligibility evaluation in the context of head and neck cancers[END_REF], and the other based on x-vector speaker embeddings [START_REF] Quintas | Automatic prediction of speech intelligibility based on x-vectors in the context of head and neck cancer[END_REF]. The same set of speakers and respective pseudo-words (see figure 3) were submitted to both approaches. A significant increase in correlation can be found in both cases. Moreover, an added degree of explainability can be obtained when using our proposed approach, since the intelligibility score can be fully traced back to the amount of similar/dissimilar consonant phones that a speaker has.

The correlation values, illustrated on table 3, also show that despite the subjectivity of the perceptual evaluations, high values can be found when using an automatic objective intelligibility estimation. This aspect mitigates the low reproducibility, variance and subjectivity of the perceptual measures in favor of a reproducible, objective and interpretable automatic prediction. The more objective way to predict speech intelligibility and the resulting added degree of explainability become highly relevant in a clinical context [START_REF] Miller | Measuring up to speech intelligibility[END_REF].

Conclusions

This paper investigated an automatic approach to predict speech intelligibility based on consonant similarity. Our approach made use of a pseudo-word task, in the context of head and neck cancer, that was force-aligned in order to train a siamese network. A base correlation of p = 0.82 was obtained between the predicted and reference values, which showed a significant correlation gain over two previous approaches. When conditioning the intelligibility prediction on certain consonants, the correlation increased up to p = 0.89. This showed that depending on the speech impairment experienced by the speaker, there are phonemes that have a greater or smaller importance in the intelligibility score. The proposed system also maintains a high degree of interpretability, since the final intelligibility score is a function of the individual scores of each phoneme, which is relevant in a clinical setting. Future work will investigate the importance of specific phonemes and co-articulations between consonants and vowels on the intelligibility score.

Figure 1 :

 1 Figure 1: Schematic diagram of the proposed system.

Table 1 :

 1 Example of a set of 52 pseudo-words with the aforementioned structure of C(C)1V1C(C)2V2. banfou bleja boucti brimpli chessant choniou clifant cogu crimpin daillu dinrant dredi fanrsi flinrpu fouma fravi gabi glunou grorvo guchin joutu juro lanvin lerda messo mouco nianlo niejo noksa nouillou pastu pidant ploniou pripin psila quiga rinta rurnu sanvrin scuna souquin spaclant sticho tangri tougzu tradrou virjant vumou yainzi yaltin zebou zouzant

Figure 2 :

 2 Figure 2: Siamese network validation heatmap. Consonant phonemes are illustrated by their respective French SAMPA symbols.

Table 3 :

 3 Correlation results achieved by the proposed methodology and by the phonetic suppression post-processing.

			p
	Automatic Speech Recognition Approach	0.63
	X-vector Speaker Embedding Approach	0.74
		Predicted	0.82
	Consonant Similarity	Predicted + Loudness	0.83
	Approach	Predicted + LogRelF0-H1-A3	0.86
		Predicted + SlopeUV0-500	0.87
		Predicted + LogRelF0-H1-A3 + SlopeUV0-500 + Loudness	0.89

Acknowledgements

This project has received funding from the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No 766287.


Andrey V Kudryavtsev

Kanty Rabenorosoa

Brahim Tamadazte

Towards a Generic Simulator for Continuum Robot Control*

I. INTRODUCTION

Nowadays, it became extremely difficult to deny the importance of continuum robotics, especially in medical applications [START_REF] Burgner-Kahrs | Continuum robots for medical applications: A survey[END_REF]. In contrast to classical industrial robots, that are typically characterized by a series of discrete rigid links, continuum robots (CR) have a particular structure going from robotic arm inspired by elephant trunk to concentric tube robots (CTR). However, there is a common property which defines them as an actuated mechanism whose backbone forms curves with continuous tangent vectors. For developing advanced control and achieving complex tasks with CR, a generic simulator would be helpful. Indeed, building a realistic experimental platform containing continuum robots may be a challenging task both in terms of time and cost. One of the most popular CR for medical applications is CTR that is obtained by assembling elementary precurved tubes with different diameters in telescopic manner. Generally, these tubes are made of Nickel Titanium alloy but more recently 3D printed ones were introduced [START_REF] Morimoto | Design of 3-D Printed Concentric Tube Robots[END_REF]. The tube assembly constitutes the effective part of the CTR and in order to get a functional robot, it is necessary to have an actuation unit including angular and linear stages. Various challenges remain to be tackled to obtain lightweight, compact, and high degrees of freedom actuation unit. Therefore, the core of this paper is the development of a generic simulation platform for CR, especially a CTR. Comparing to widelyused simulation platforms such as V-Rep or Gazebo, our simulator has an important advantage: it is possible, and easy, to add new deformable robots. Actually, the simulator can be used, for instance, to optimize the CTR mechanism design, to develop and validate advanced controls by using various feedbacks, and to achieve complex tasks by integrating anatomical models, etc. In the reminder of this paper, at first the software architecture is presented. Secondly, we discuss the capabilities of the simulator from the user point of view, i.e., what a person with no background in programming can achieve with it.

II. SOFTWARE ARCHITECTURE

The simulator presented here is written mostly in JavaScript (JS). JS is a web-oriented programming language supporting object-oriented paradigm. Inspite of the fact that JS is generally used on web-pages, since 2009 it became *This work has been supported by the Labex ACTION project (contract "ANR-11-LABX-0001-01") and ANR NEMRO (contract "ANR-14-CE17-0013"). brahim.tamadazte@femto-st.fr (corresponding author) Fig. 1. Main window screenshot of the simulator displaying: a 3D scene with CTR, target object, and an image coming from the virtual camera installed on the end-effector.

possible to develop stand-alone desktop applications thanks to two following elements:

• runtime environment called Node.js which allows executing JS code without a browser; • framework Electron that made possible building desktop applications using web-development technology (HTML, CSS, and JS). The fact of using an object-oriented structure adds very interesting features to our simulator. In particular, in order to add a new robot, one has only to create a new class containing the robot forward kinematics model.

III. USER-SIMULATOR INTERACTION

After the application starts, a user will see the main window displayed in Fig. 1. It contains the following elements:

• 3D scene containing CTR and a target object (white box with four black dots) in front of it. • a menu allowing to change some parameters of the scene. In the given example, one can modify joint variables (mouse-based control) associated with the CTR; • controls of server parameters defining the IP address and port number allowing the communication of simulator robot SETJOINTPOSREL,0,0,0,0,0,0.001 GETJOINTPOS STOP GETIMAGE camera GETCALIBMAT with the external world (external controller, software, etc.) via UDP communication;

• buttons for scenes management (loading a new scene or reloading the current one). Also, the simulator gives the possibility to place a virtual camera either on the robot end-effector or freely in 3D space. This gives the possibility to generate a virtual image with known and chosen camera model (e.g., intrinsic parameters). This last point is of particular interest for simulating visionbased control schemes such as visual servoing [START_REF] Baran | Preliminary results on OCT-based position control of a concentric tube robot[END_REF] or automatic intracorporeal navigation [START_REF] Kudryavtsev | Eye-in-Hand Visual Servoing of Concentric Tube Robots[END_REF].

A. Constructing the Scene

The 3D scene of the simulator must be defined in advance by a user and can contain three types of objects. Firstly, it may contain different static objects that are loaded from standard 3D-model files such as *.stl, *.obj or *.dae files. Secondly, it is a robot with its joint values that are directly accessible from the menu in the right-up corner. A scene may contain several robots. Finally, the third type of components concerns imaging systems such a standard white-light camera which allows generating images that can be then acquired using UDP protocol. The whole scene is defined using a text file in json format 1 .

B. Robot Simulation

During simulation, a user has access to two tools allowing the interaction with the scene. The first one is the menu that allows some basic manipulations such as changing joint values, displaying or hiding frames associated with robot joints, etc.

The second tool is a UDP protocol. Actually, the simulator is a UDP server that can send and receive messages from external world. Among current possibilities, a user can change robot configuration (relative or absolute joint position), apply joint velocities, get the transformation matrix corresponding to the tool pose, acquire the image from a virtual camera, etc. Some examples of commands are represented in Table I.

There is a variety of ways for sending these UDP messages to the simulator. The easiest one is to use a small software telnet which allows sending UDP messages directly from terminal. Another way would be to use a telnet equivalent software with a graphic user interface. Finally, it is always possible to establish UDP communication using different 1 for details, see https://github.com/avkudr/visa/wiki/Scene-modeling Fig. 2. Simulating a scene containing a 3D face with a nasal cavity, and a CTR. Images on the right generated by the camera located on the robot tip.

programming languages: python, MATLAB, etc. It is worth noticing, that an adapter for C++ communication is provided along with the simulator. This adapter, while acting like a UDP client, allows in addition to transform the image coming from simulator directly to OpenCV or ViSP libraries format.

C. Example of use

In this subsection, we present an example of a complete simulated scene (Fig. 2). The scene contains a part of human face as well as the 3D model of nasal cavity. Both objects were loaded from *.stl files. CTR is placed in a way allowing entering the cavity and performing an internal navigation. In the given example, a CTR model is based on piecewise constant curvature model. In addition, a camera is located on the robot tip which enables online generation of images. This setup can be used for development and testing of various visual servoing and/or SLAM algorithms.

IV. CONCLUSION

The goal of this work was to create a simulation environment for continuum robots. Nowadays, it contains already a concentric tube robot build using constant-curvature model. Moreover, thanks to simulator flexibility of adding new robots, one can use the existing pattern to build other robots and add them to 3D scene. As user can also acquire the image given by the virtual camera in real time, it opens new possibilities for testing such algorithms as visual servoing, SLAM or any other technique based on a visual feedback.


Jean-Baptiste Camps

Christelle Chaillou

Viola Mariotti

Federico Saviotti

Textual, Metrical and Musical Stylometry of the Trouvères Songs

The lyrical tradition and the Chansonnier du roi

In the landscape of medieval French texts' authorship, the lyrical poetry of the trouveres and trouveresses has the aspect of an exception: contrarily to what can be observed elsewhere, anonymity is rare, and the manuscripts mostly take care to give a clear (though often conflicting between them) attribution, even up to the point of adopting a material organisation by author (fig. 1). Yet, very little is known about the attribution of the melodies that accompany many of these texts: how often are they original? Were they composed by the trouvere himself, or by one or several other composers? Is there even a unity behind the different melodies of the work of a given author?

To answer these questions, we propose to experiment in the cross examination of the text, the metrical composition and the melodies of a corpus of lyrical texts with the methods of computational stylometry. We will focus on the Manuscrit du Roi (Paris, BnF fr. 844), one of the earliest (1260-1270) and richest sources for Romance lyric poetry with its 602 compositions. The literary, musical and linguistic diversity of its contents (profane songs of French trouvères and Occitan troubadours, French motets, instrumental works and Latin sacred compositions) makes it a unique case-study.

1

The contents of this manuscript were acquired thanks to a workflow going from layout analysis, handwritten text recognition to digital editing and ultimately stylometric analysis. Text and music are encoded following the Guidelines of, respectively, the Text Encoding Initiative (TEI, 2020) and the Music Encoding Initiative (MEI, 2020), resulting in a textual and musical edition (See, for a detailed presentation of the ongoing editorial process, Camps et al., 2021a).

For the needs of the textual analysis of the text, the HTR output, after careful human correction, was then segmented into words, normalised (abbreviations expanded, allographs normalised), lemmatised and POS-tagged using deep learning methods (Table 1). Even if the scores obtained are high, cumulative errors through the different steps might remain an issue. The availability of a complete transcription of both text and musical notations paves the way for the stylometric analysis of the songs of the trouvères and trouveresses, at a level impossible until now. This is a critical issue, because disputed attributions are very numerous inside the Old French Lyrical tradition [START_REF] Gatti | Repertorio delle attribuzioni discordanti nella lirica trovierica[END_REF]), yet it poses specific challenges, because the components are very short (often less than 50 verses), idiolect of the text appears rather homogeneoussharing as they are on surface the same elitist cultural tradition -, while, in the meantime, the manuscript tradition creates noise (linguistic and substantial variants, even between scribes of the same manuscript). Moreover, the field of musical stylometry is still quite new, and attribution of the text and of the melody have rarely -if ever -been addressed as a whole in this tradition.

Step

By default, scholars generally postulate that the melody and the text of a song were composed by the same person. Yet the attested musical elements (for exemple : musical curbe, modality, intervals, ambitus, motives, melismas and musical form) have usually been considered as much more influenced by the tradition than the textual ones. Nonetheless, it will be worth testing the hypothesis that song attribution may be consistently built on both textual and musical elements.

Specifically, the Chansonnier du roi offers an interesting dossier, because it contains a subunit dedicated to the collected work of a single author (Thibaut de Champagne so-called Liederbuch). Thibaut is perhaps the most prominent of all trouvères (Barbieri, 1999), and the attribution of several songs to him is still disputed [START_REF] Wallensköld | [END_REF]Callahan, 2010).

Stylometric analysis of the text

In order to give new insights into these disputed attributions, we performed several stylometric analyses of the text and the music.

Stylometric analysis of the text has initially been done using features robust to noise and short text length, in particular character 3-grams (Camps et al., 2021a). Both exploratory and supervised analyses, the latter using a linear SVM, were performed to shed more light on the attribution of these components. To compensate for the imbalance between the available samples for all four authors, various upsampling and downsampling strategies (alone or in combination) were experimented, and Tomek links removal [START_REF] Tomek | Two modifications of CNN[END_REF], in combination with class weights, was found to be the best performing. Evaluation was done using a leave-one-out approach, and the models reached a global accuracy of 0.79, with a F1 score of 0.93 for Thibaut and 0.76 for Gace ( Yet, the extraction of the features with the highest coefficients in the SVM models show a more contrasted result. For an author like Gace Brulé, the 3-grams seem to reveal aspects significant for our stylometric analysis and reflecting lexical and morphological preferences of the author. For instance, the high-frequency of apl, usp depends mainly of the syntagm a plaisir (written as one single word) and the words souspir/souspirer respectively, which seem to be typical of Gace's idiolect, whereas the low-frequency ete is consistent with the raffinate poet's avoidance of the diminutive nouns and adjectives in -ete, as marks of a popular register, and his apparent rare use of some abstract nouns like faussete. Yet for Thibaut's corpus, the analysis of the most and least recurrent 3-grams seems to include features that are not of an authorial nature but reflect the particular graphemic habits of the copyist of the part of the manuscript known as Thibaut's Liederbuch as opposed to the scribes at work in the rest of the manuscript: use of ‹o› instead of ‹ou› in particular in words like po(u)r, to(u)z, amo(u)rs, use of ‹u›, not ‹v› at the beginning of words (this last feature being even of a more graphetic nature).

To bypass this inconvenient, analyses were repeated, using lemmas, part-of-speech 3-grams and lemmatised function words. Results actually were found to be less accurate (Table 2), which can either point to a less informative nature of these features (i.e., excessive suppression of information and noise generation through lemmatisation, tagging and function words selection), or to the fact that some of the performance of the 3-grams model is due to correlation between scribal practice and authorial units. If that were true, normalisations could both potentially decrease the performance of the model and increase the authorial nature of the features it uses. The relative better performance of lemmas (function and content words) in comparison to part-of-speech or function lemmas could also point towards the importance of lexical choices to discriminate between authors in the training material.

Stylometric analysis of the music

Musical stylometry (especially of the trouvères) is a much less waymarked field. It is supposed that all composers integrate some little elements consciously or unconsciously in their compositions. A recent study observed that the troubadour Bernard de Ventadorn used in most of this songs Figure 2: Features with the 10 highest positive and negative coefficients in the linear SVM models for Gace Brulé and Thibaut de Champagne; the higher the coefficient, the more the feature would contribute to a placement on one side of the separating hyperplane or the other three ascendants sounds at the beginning of verses (Chaillou-Amadieu 2016). Such methodology can be automated and show promise for the joint stylometry of text and music. In order to identify relevant features, experiments have been conducted with a variety of variables: notes, intervals, octaves n-grams, with the inclusion of syllable boundaries in the absence of noted measure, all extracted from MEI neumes notation. They were done on two different dataset: a small, controlled dataset (3×10 melodies of supposedly three authors), and an enlarged and more difficult dataset (117 melodies, with imbalanced training set of six authors). Intervals or notes bigrams have proved to be the most efficient on the smaller set, while results on the larger set remain more difficult to interpret. A visualisation on the smaller set shows how these features can structure a vector space of partly authorial data clouds (fig. 3), yet a benchmark of the various types of features for supervised learning gives results whose interpretation is more elusive (Table 3), apart from the obvious impact of availability of training samples (Table 4). Yet, important work remains to be done, on one hand, to measure the relevancy of different features both for textual and musical stylometry, going beyond simple metrics. Indeed, our work results stress the necessity to go beyond bare metrics in the evaluation of supervised model training, to actually consider the precise features on which the model bases its attributions.

Moreover, we aim to explore features that are at the intersection of text and music, such as recurring associations between certain musical and textual features, as well as metric patterns.

Bibliography

Figure 1 :

 1 Figure 1: BnF, fr. 844, fol. 29 (manuscrit du roi), beginning of the section for the texts of the count of Bar.

Figure 3 :

 3 Figure 3: First factor plane from principal component analysis of notes and intervals bigrams (smaller set)

  Callahan, C. (2010). 'Thibaut de Champagne and Disputed Attributions: The Case of MSS Bern, Burgerbibliothek 389 (C) and Paris, BnF fr. 1591(R)'. Textual Cultures, 5(1). Indiana University Press: 111-32 doi:10.2979/tex.2010.5.1.111. Camps, J. B., Clérice, T., & Pinche, A. (2021a). 'Noisy medieval data, from digitized manuscript to stylometric analysis: Evaluating Paul Meyer's hagiographic hypothesis'. Digital Scholarship in the Humanities, 36(Supplement_2), ii49-ii71. Camps, J.-B., Chaillou, C., Mariotti, V. and Saviotti, F. (2021b). 'Editing and Attributing Musical Texts: the Chansonnier du Roi and the MARITEM Project'. EADH2021: Interdisciplinary Perspectives on Data, 2nd International Conference of the European Association for Digital Humanities, Krasnoyarsk, 2021 https://halshs.archives-ouvertes.fr/halshs-

Table 1 :

 1 Word accuracy is indicated with respect to the in-domain test set (samples set apart from training material).

		Word acc	Engine	Data/Model
	segmentation	96.71	Boudams (Clérice, 2020)	10.5281/zenodo.6500604
	normalisation	98.01	Pie (Manjavacas et al., 2019) 10.5281/zenodo.6500649
	lemmatisation	97.66	Pie (ibid.)	10.5281/zenodo.4320487
	pos-tagging	97.55	Pie (ibid.)	10.5281/zenodo.4320487

Table 2

 2 ).

			CHAR 3-GRAMS				LEMMAS (ALL)
		precision recall	f1	support		precision recall	f1	support
	Blond	0.85	0.52	0.65	21	Blond	0.77	0.48	0.59	21
	Gace	0.69	0.86	0.76	43	Gace	0.70	0.65	0.67	43
	Gaut	0.71	0.60	0.65	20	Gaut	0.85	0.55	0.67	20
	Thib	0.93	0.93	0.93	45	Thib	0.65	0.91	0.76	45
	-					-
	accuracy			0.79	129	accuracy			0.70	129
	macro avg	0.79	0.73	0.75	129	macro avg	0.74	0.65	0.67	129
	weighted avg	0.80	0.79	0.79	129	weighted avg	0.72	0.70	0.69	129
			POS 3-GRAMS			LEMMAS (FUNCTION WORDS)
		precision recall	f1	support		precision recall	f1	support
	Blond	1.00	0.24	0.38	21	Blond	0.59	0.48	0.53	21
	Gace	0.43	0.49	0.46	43	Gace	0.45	0.40	0.42	43
	Gaut	0.17	0.05	0.08	20	Gaut	0.25	0.30	0.27	20
	Thib	0.43	0.67	0.53	45	Thib	0.62	0.69	0.65	45
	-					-
	accuracy			0.44	129	accuracy			0.50	129
	macro avg	0.51	0.36	0.36	129	macro avg	0.48	0.47	0.47	129
	weighted avg	0.48	0.44	0.41	129	weighted avg	0.50	0.50	0.50	129

Table 2 :

 2 Results of the leave-one-out evaluation on the text of the songs, using character 3-grams, lemmas, part-ofspeech 3-grams and lemmatised function words

Table 3 :

 3 Benchmark of musical features for musical stylometry

				SMALLER SET
	n	1	2	3	4	5	6
	notes	0.67 0.80 0.73 0.67 0.63 0.70
	octaves	0.57 0.53 0.57 0.60 0.57 0.63
	intervals 0.77 0.80 0.73 0.70 0.70 0.53
				LARGER SET
	n	1	2	3	4	5	6
	notes	0.29 0.38 0.38 0.38 0.26 0.26
	octaves	0.31 0.44 0.47 0.41 0.38 0.47
	intervals 0.37 0.40 0.41 0.43 0.46 0.40

Table 4 :

 4 Detailed leave-one-out evaluation of the SVM linear models on intervals bigrams from the larger set (with random downsampling)

		precision recall	f1	support
	BlonNes	0.21	0.19	0.20	16
	GaBru	0.41	0.35	0.38	34
	GautDarg	0.29	0.31	0.30	16
	GuilVinier	0.29	0.33	0.31	12
	Moniot	0.50	0.75	0.60	12
	ThibChamp	0.68	0.63	0.65	27
	-
	accuracy			0.43	117
	macro avg	0.40	0.43	0.41	117
	weighted avg	0.43	0.43	0.42	117

Discussion and further researchInitial results show good and average performance on the attribution of texts and melodies, with an interesting better performance for Thibaut and Gace, our two main target authors.