Nancy Bertin
email: nancy.bertin@irisa.fr

Ewen Camberlein

Romain Lebarbenchon

Emmanuel Vincent

Sunit Sivasankaran

Irina Illina

Frédéric Bimbot

VoiceHome-2, an extended corpus

Keywords: distant-microphone, reverberation, noise, robustness, localization, enhancement, ASR

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Introduction

On October 18 th , 2016, Microsoft announced that their automatic speech recognition (ASR) technology named Cortana had "reached human parity" in conversational speech recognition, a "historical achievement", to borrow the words of the press release 1 , followed by a large number of articles in the general press and supported by later publications [START_REF] Xiong | Achieving human parity in conversational speech recognition[END_REF]. As system performance has increased in the last few years, notably thanks to the introduction of deep learning based technologies, commercial products have reached the market and the interest for the numerous applications of speech technology has grown: applications on mobile phones or tablets, games and toys, conference call systems, hands-free systems, hearing aids and other aids for people with disabilities, smart homes, etc.

In order to accompany the development and deployment of such end-user applications with wider and wider usage scenarios, it is now impossible to limit oneself to near-field capture of the user's speech. While this remains the most frequent use case today, speaker localization, speech enhancement, and ASR in distantmicrophone scenarios remain challenging [START_REF] Baker | Research developments and directions in speech recognition and understanding, part 1[END_REF][START_REF] Wölfel | Distant Speech Recognition[END_REF]Cohen et al., 2010;Virtanen et al., 2012;[START_REF] Li | Robust Automatic Speech Recognition -A Bridge to Practical Applications[END_REF]Vincent et al., 2018).

The development of robust techniques able to alleviate reverberation and noise, the main challenges of distant-speech processing "in the wild", requires suitable corpora for development and testing. A number of real corpora are now publicly available for environments and application scenarios such as voice command for cars (Aurora-3, 2000;[START_REF] Hansen | CU-Move": Analysis & corpus development for interactive in-vehicle speech systems[END_REF][START_REF] Lee | AVICAR: audio-visual speech corpus in a car environment[END_REF] and in public spaces [START_REF] Barker | The third 'CHiME' speech separation and recognition challenge: Dataset, task and baselines[END_REF], automatic transcription of lectures [START_REF] Lamel | The translingual English database (TED)[END_REF], meetings [START_REF] Janin | The ICSI meeting corpus[END_REF][START_REF] Mostefa | The CHIL audiovisual corpus for lecture and meeting analysis inside smart rooms[END_REF][START_REF] Renals | Interpretation of multiparty meetings: The AMI and AMIDA projects[END_REF], dialogs [START_REF] Llsec | Lincoln laboratory speech enhancement corpus[END_REF][START_REF] Stupakov | The design and collection of COSINE, a multi-microphone in situ speech corpus recorded in noisy environments[END_REF] and other public gatherings [START_REF] Lincoln | The multi-channel Wall Street Journal audio visual corpus (MC-WSJ-AV): Specification and initial experiments[END_REF][START_REF] Fox | The Sheffield wargames corpus[END_REF], and automatic transcription of noisy or overlapped speech in broadcast media [START_REF] Gravier | The ETAPE corpus for the evaluation of speech-based TV content processing in the French language[END_REF][START_REF] Bell | The MGB challenge: Evaluating multi-genre broadcast media recognition[END_REF].

More recently, distant-microphone speech processing in domestic environments has drawn much interest. Emblematic devices such as Amazon Echo or Google Home embody this interest, which is explained not only by the financial stakes behind voice-controlled home automation and multimedia systems (or other applications such as human-robot communication, speech monitoring and surveillance systems, among others) but also by the difficult challenges raised by these environments. For instance, the reverberation time is typically higher than in, e.g., car or office environments. Talkers are located at variable distances from the microphone, from a few centimeters up to several meters. Noise backgrounds are often highly nonstationary and complex, due to the overlap of multiple noise sources such as competing talkers, TV/radio, footsteps, doors, kitchenware, electrical appliances, noise from outside, among others.

Comparable corpora

The CHiME series of challenges and corpora have contributed to popularizing research on robust speech processing in domestic environments. The first two corpora, CHiME-1 [START_REF] Barker | The PASCAL CHiME speech separation and recognition challenge[END_REF] and CHiME-2 [START_REF] Vincent | The second CHiME speech separation and recognition challenge: An overview of challenge systems and outcomes[END_REF] feature real noise backgrounds collected in daily situations in a family home over the course of several weeks. Reverberation was generated by convolving clean speech with time-varying room impulse responses recorded in the same home using a binaural microphone setup. Reverberated speech was then scaled so as to match the intensity of normal voice at a distance of 2 m and added to randomly selected noise segments.

The DIRHA Simulated corpus [START_REF] Cristoforetti | The DIRHA simulated corpus[END_REF] was generated in a similar way, with more microphones across several rooms and simulated noise backgrounds obtained by summing individually recorded noises. Both corpora were released with baseline software tools [START_REF] Vincent | The second CHiME speech separation and recognition challenge: An overview of challenge systems and outcomes[END_REF][START_REF] Brutti | A speech event detection and localization task for multiroom environments[END_REF]. These corpora are realistic in several aspects and, as such, they promoted significant advances in the field. Yet, they differ from speech collected in real, ecological situations in several other aspects. For instance, in the real world, the intensity and stress level of speech depend on the amount of reverberation and noise and on the distance.

Few speech corpora have been collected in real homes so far. The DICIT corpus [START_REF] Brutti | WOZ acoustic data collection for interactive TV[END_REF]) features a constrained scenario, with talkers sitting in front of a smart TV. The DIRHA-English corpus [START_REF] Ravanelli | The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments[END_REF] and the Sweet-Home corpus [START_REF] Vacher | The Sweet-Home speech and multimodal corpus for home automation interaction[END_REF] relax this constraint, but are not publicly available 2 . The DIRHA_AEC corpus [START_REF] Zwyssig | A multi-channel corpus for distant-speech interaction in presence of known interferences[END_REF], specifically designed for echo cancellation, includes semi-simulated data and real data, with a larger number of scenarios of increasing complexity, but a limited number of noise conditions. The ATHENA corpus [START_REF] Tsiami | ATHENA: a Greek multi-sensory database for home automation control[END_REF] provides real noisy speech data, together with video and Kinect recordings, with a larger diversity (20 speakers, 16 speaker positions, 20 noise conditions, heterogeneous and spatially distributed microphone network), recorded mostly in one room, plus 2 speaker positions and 2 microphones in a second. Crucially, all these recordings were made in a single home. This precludes the use of machine learning techniques (e.g., based on deep neural networks) for speech enhancement and ASR [START_REF] Weninger | Speech enhancement with LSTM recurrent neural networks and its application to noise-robust ASR[END_REF][START_REF] Yu | Automatic Speech Recognition -A Deep Learning Approach[END_REF], which require data collected in distinct homes for training, validation, and testing. In addition, none of these corpora provides data in French, which is, according to the French government 3 , the sixth most spoken language in the world (220 million speakers, 77 million native speakers, official language in 29 countries.)

The voiceHome and voiceHome-2 corpora

Motivated by these observations, we introduced in 2016 a new corpus named voiceHome [START_REF] Bertin | A French corpus for distant-microphone speech processing in real homes[END_REF] for distant-microphone speech processing in domestic environments, which is publicly available at no cost4 . Developed in the scope of the voiceHome project, whose target application is distant-microphone command of multimedia and smart home appliances via natural dialog, its first release included live speech from 3 native French talkers in reverberant and noisy conditions recorded in a smart room experimental facility furnished and equipped to mimic a real home5 , as well as room impulse responses and noise signals recorded in various homes with an 8-microphone device.

In order to enrich this corpus, as initially planned, we collected and annotated more data, including a larger number of homes, rooms and speakers. The result of this new campaign constitutes the voiceHome-2 corpus presented in this paper. For homogeneity and usability reasons, data from the first voiceHome corpus was not included in voiceHome-2. Some notable differences can be emphasized to explain this choice:

• In the voiceHome corpus, each speaker uttered the same sentence at 5 positions in each room, leading to little lexical and phonological diversity, and possible biases in experimental results if the corpus was to be separated in training and test subsets. In voiceHome-2, all uttered sentences are different.

• The duration of noise-only segments before and after each utterance was downsized (from 15 s before and after the utterance in voiceHome, to 5 s before and a short, variable duration after in voiceHome-2) to reduce recording time and storage size, while experimentally preserving performance.

• Two speakers would have appeared in two different homes if the two corpora had been merged. By keeping them separate, each speaker appears only in one home in voiceHome-2.

• In voiceHome-2, we recorded noisy speech in 3 rooms in each home, compared to only 1 in voiceHome.

• VoiceHome-2 includes 2 utterances per speaker and noise condition instead of 5 in voiceHome, which allows a larger variety of noises and rooms for the same total recording duration.

• VoiceHome-2 includes spontaneous speech. Only short, read utterances were recorded in voiceHome.

It must be noted that all voiceHome-2 data have been recorded in different homes than those used for voiceHome. The newly recorded data do not include additional room impulse responses and noise-only data. Indeed, the room impulse responses and the noise-only data in voiceHome can be used to generate simulated data for training [START_REF] Ravanelli | Realistic multi-microphone data simulation for distant speech recognition[END_REF] while the new, real voiceHome-2 data can be used for testing. Table 1 comparatively summarizes the main features of the two corpora. All the above mentioned differences make the voiceHome-2 corpus a new corpus, which is described in detail in the rest of this paper. Used together as complementary, companion corpora, voiceHome and voiceHome-2 constitute a complete, self-contained dataset allowing for training, development and test of robust speech processing techniques. In this paper, particular emphasis is put on the new data from the voiceHome-2 corpus, while only necessary reminders about voiceHome are included. In Section 2, we present the corpus specifications and annotations, the recording protocol, and an exhaustive description of the newly recorded data. In Sections 3 and 4, we describe the baseline software tools for source localization and for speech enhancement and ASR, respectively, and the resulting performance. We conclude in Section 5 by outlining possible future uses and applications of this data.

Specifications, recording and annotations

The voiceHome-2 corpus contains audio recordings, annotations and transcriptions of speech utterances from several speakers in various realistic, reverberant and noisy domestic environments.

Put together, the two corpora gather various types of audio signals:

• room impulse responses (voiceHome),

• noise-only signals (voiceHome),

• short reverberated, noisy utterances following a scenario of home automation or multimedia appliance control (voiceHome, voiceHome-2),

• clean utterances of the same nature (voiceHome, voiceHome-2),

• spontaneous speech (voiceHome-2).

Specifications

The short utterances and the spontaneous speech in voiceHome-2 were recorded in 4 different homes. Each home was assigned to a group of 3 speakers (2 males, 1 female). In each home, recordings were performed in 3 different rooms, for a total of 12 different rooms. Each speaker assigned to a given home was recorded in each of the 3 rooms of the home.

Short utterances

The short utterances were generated from two distinct grammars, one for home automation applications and one for multimedia applications, designed from industry specifications and user studies and spanning the basic functionalities expected in a smart home. Both grammars were written in the Augmented Backus-Naur Form (ABNF) format, respecting the W3C Speech Recognition Grammar Specification V1.0 standard6 . All utterances start with the keyword « OK Vesta » to allow for future wake-up-word technology deployment. This keyword is followed either by: • a question: « Qu'est-ce qu'il y a ce matin à la télé ? » ("What's on TV this morning?"),

• a wish: « Je veux éteindre tous les luminaires. » ("I want to switch all the lights off."),

• a command: « Redémarre le programme ! » ("Restart the program!"), with possible adjuncts of time, space, or other adjuncts specifying the query. A small number of utterances are short commands (« Pause ! » -"Pause!") or possible followups to a first round of dialog (« Et sur France 2 ? » -"And what about Channel 2?").

The vocabulary includes 345 words, including a few named entities (mostly names of French TV channels). The dataset only contains utterances generated by the grammar (no true negative), as spontaneous speech excerpts can be used for this purpose. All sentences in the corpus differ from each other, resulting in a total of 1560 different utterances. Thus, each sentence can be straightforwardly associated to a quintuplet: home, room, speaker, speaker position, and noise condition.

Each recording chronologically contains 5 s of the predetermined noise condition, then the « OK Vesta » keyword immediately followed by the specific sentence uttered, and finally a short noise of variable duration depending on when the operator turned the recording off.

Spontaneous speech

In addition, spontaneous speech was also collected in each room. The speakers were asked to pick one topic among a list of suggested topics (your best vacation, a cooking recipe, etc.) or to choose one from their own imagination. They were given free time to prepare themselves and, once ready, spoke about the selected topic during 2 min.

Noises and room impulse responses

We remind here some information about the recordings from voiceHome, that may act as a companion training dataset for the new data.

Impulse responses were obtained by processing recordings of a 6 s chirp from 0 to 8 kHz, played by a loudspeaker7 , in 12 different rooms of 3 real homes (4 rooms per home: living room, kitchen, bedroom, bathroom). In each room, recordings were performed for 2 different positions of the microphone array and 7 to 9 different positions of the loudspeaker. These positions span a range of angles and are distributed logarithmically across distance. The recordings were then convolved with the inverse chirp to obtain the estimated room impulse responses.

In addition, in each of the 12 rooms, 5 complex, everyday noise scenes relevant to the function of the room (background speech, television, footsteps, meal preparation, shutters opening or closing, water flowing and so on) were recorded, at the same 2 array positions as above. The noise sources are different in each home.

We recall that no home nor room is shared between the voiceHome and voiceHome-2 corpora.

Speech recording protocol

With the exception of clean speech, all audio data were recorded by means of 8 MEMS microphones 8 plugged on the faces of a 10 cm cube (see Fig. 1), and encoded as 16-bit, 16 kHz, 8-channel WAV files. A USB interface allows direct digital recording from the array to a computer.

Speech data were recorded at a pace of one daily session per home. The relative position of the microphones on the cube was fixed during the whole session. In order to ensure realism, the noise conditions were decided at the beginning of each recording session. They consisted of 1 "quiet" and 3 "noisy" conditions per room, determined by selecting objects present in the room and respecting the room's function and natural usage (hairdryer in the bathroom, cooking appliances in the kitchen, and so on). 5 speaker positions per room, including standing and sitting postures, were chosen to cover a large range of angles and distances with respect to the microphone array, which was placed at a single, fixed position for all the recordings in the room. The speaker positions were marked with duct tape and their coordinates were measured with a laser telemeter and written down, as well as the noise source locations, before proceeding with the recordings.

In each home, each of the 3 speakers (2 males, 1 female) was assigned a list of 120 sentences. He/she was equipped with a tie-clip microphone to ease the transcription process 9 and the approximate height of his/her mouth (sitting and standing) was measured. In order to avoid additional uncontrolled noise, only 2 people were present in the room besides the speaker: one operator who turned recording on and off from the control laptop, and one operator who produced the desired noise condition. The 2 other speakers for the session stayed in another room and remained as quiet as possible to avoid additional background noise.

The recordings were collected in the following order:

1. room by room; 2. in a given room, speaker by speaker; 3. in a given room and for a given speaker, position by position; 4. for a given speaker at a given position in the room, noise condition by noise condition.

For each position and noise condition, each speaker was asked to utter 2 sentences, one from the home automation grammar and one from the multimedia appliance control grammar, starting at the recording operator's hand sign (after recording 5 s of noise). The speakers were asked to face the microphone array and to avoid moving away from the target position, which is realistic given the short duration of the utterances, so that their actual position matches the annotation. They were however given no instruction regarding the way to speak. As a result, they adapted their voice in different, natural ways against reverberation, noise, and distance.

Once all 40 sentences had been recorded for a given speaker in a room, the speaker was assigned one position and one noise condition randomly drawn from the 5 positions and the 4 conditions associated with the room, excluding the "quiet" condition, and spontaneous speech was recorded for that speaker for approximately 2 min. These random assignments were constrained so that, in a given room, the 3 speakers were recorded at different positions and under different noise conditions.

The operation was repeated speaker by speaker and eventually room by room. In addition, all speakers were asked to utter 10 different sentences from the same grammars in clean conditions. These clean data were recorded in noiseless, low reverberation, close-microphone conditions with a single-channel AKG CK91 microphone with a pop filter and an AKG SE300B pre-amplifier.

All speakers signed a consent form for recording, dissemination and use of the corpus for research purposes.

Annotations and transcriptions

The experimental settings were documented in a series of annotation files describing:

• The global position of the microphone array and its orientation (in the room coordinate system);

• The positions of the 8 microphones (in the array coordinate system);

• The speaker position (free text description, such as "sitting on the couch", coordinates of the mouth in the room coordinate system, orientation of the mouth in azimuth and elevation);

• The type of the room (free text description, such as "kitchen");

• The noise condition (noise type, approximate noise position when fixed and known).

For speech data, the transcriptions include the start and end time of each utterance (noise-only segments being labeled as [$NO_SPEECH]), the sentence that the speaker was asked to read (prompt), and an accurate transcription of what he/she actually uttered. The transcriptions were manually reviewed by listening to the clip-tie microphone recording. All text files are encoded in UTF-8.

File naming conventions, documentation, and download

Directory structure and file naming conventions of the dataset have been made explicit to allow for easy use and automated parsing. The structure is identical to the first voiceHome corpus, except for the removal of the subdirectories audio/rirs/ (room impulse responses) and audio/noises/ (noise-only signals) and the addition of audio/spontaneous/ and transcriptions/spontaneous/ (spontaneous speech). Each file of the dataset, irrespective of its nature, follows a naming convention describing its contents, built from a general pattern from which irrelevant fields are discarded. Each audio file can be automatically matched with the corresponding annotations and transcriptions. Filetypes depend on the contents (.wav for audio files, .txt for annotations, .pdf for documentation).

An exhaustive list of filenames and information about each field can be found in the documentation, which can be downloaded separately or together with the data. The complete corpus and the documentation can be freely downloaded at no cost at https://zenodo.org/record/1252143.

Summary contents

Overall, the voiceHome-2 corpus contains:

• 120 clean utterances, 360 reverberated utterances in quiet, and 1080 reverberated utterances in noise from 12 different speakers recorded in 12 rooms distributed in 4 real homes, for a total duration of about 4 h (including background noise before and after the actual utterance);

• 36 chunks of spontaneous speech of 2 min each, for the same speakers and rooms;

• full annotations for all data.

Together with the voiceHome corpus, the two corpora form a complete set allowing for training, development and testing of speech processing applications. To highlight the complementarity of the two corpora, let us recall the contents of the voiceHome corpus:

• 8-channel impulse responses from 12 different rooms of 3 real homes,

• 120 min of various noises recorded in the same rooms,

• 60 clean utterances, 75 reverberated utterances in quiet and 225 reverberated utterances in noise from 3 different speakers, for a total duration of about 2.5 h.

In particular, the corpora include challenging situations (high reverberation, low signal-to-noise ratio, nonstationary noises with variable position or diffuse spatial distribution, obstacles between the speaker and the microphone array), which makes them particularly suited for the development and testing of nextgeneration speech processing techniques. The diversity and realism of room types and noise conditions can be assessed from the list in Table 2. Reverberated noisy speech is mainly intended for the testing of source localization, speech enhancement and ASR, while clean speech, impulse responses and noise-only signals are intended for generating simulated data for training.

Multichannel source localization

In addition to the above data, we distribute baseline software tools for source localization and for speech enhancement and ASR. Indeed, the direction-of-arrival (DOA) of the speaker with respect to the microphone array is a valuable piece of information for subsequent signal processing, in particular for certain speech enhancement and ASR methods.

Baseline Localization System

As a baseline for speaker localization, we use our own implementation of the state-of-the-art steered response power with phase transform (SRP-PHAT) algorithm [START_REF] Dibiase | Robust localization in reverberant rooms[END_REF]. We made this implementation freely available, together with 7 other angular spectrum-based localization techniques [START_REF] Blandin | Multi-source TDOA estimation in reverberant audio using angular spectra and clustering[END_REF], in a Matlab toolbox named Multichannel BSS Locate10 .

The general principle of SRP-PHAT is to compute a function Φ(θ, ϕ) termed "angular spectrum", where θ and φ are azimuth and elevation variables, which is expected to exhibit local maxima in the directions of active sources. The angular spectrum is first computed for each microphone pair and then aggregated across pairs. The computation consists of the following steps:

1. Define the search space, i.e. a grid of possible DOAs (θ j , ϕ k ) for which we want to evaluate Φ in the global coordinate system; 2. For each microphone pair n:

(a) Compute the corresponding angles of arrival (AOAs) {α i ) [START_REF] Knapp | The generalized cross-correlation method for estimation of time delay[END_REF] jk ) over all time frames t and across all microphone pairs n. A pooling method as maximum or sum) must be chosen for this purpose. 4. Find the indexes j and k of the largest peak (single source case) or peaks (multiple source case) of Φ(θ j , ϕ k ), yielding the estimated source azimuth(s) θ j and elevation(s) ϕ k .

-1 ------→ Mn 1 Mn 2 is the vector defined by the considered microphone pair. The DOA (θ j , ϕ k ) and the corresponding AOA α jk for this microphone pair are illustrated for one point on the sphere. The number of sampled azimuths is constant per elevation.

0 1 -1 0 1 -1 0 1 -----→ M n1 M n2 (θ j , ϕ k ) -→ P α jk x y z
The sampling of the DOA sphere and the computation of the AOA are illustrated in Fig. 2. More details about interpolation and pooling can be found in the toolbox documentation. These operations, as well as the definition of the search space, can be tuned by the user and depends on the desired resolution. Experimental results presented in Sec. 3.3 were obtained by limiting the search space to the upper half sphere, sampling it at a resolution of 1°in azimuth and elevation in the global frame, then resampling them at 5°in the microphone pair local frame. This results in a subsampling factor of approximately 32. The application of SRP-PHAT to one noisy speech file of the voiceHome-2 corpus is illustrated in Fig. 3. The top four plots represent the local angular spectra for four different pairs of microphones. The "projection" in the global coordinate system of the cone of confusion that defines the intrinsic localization indeterminacy with 2 microphones is clearly visible. The bottom plot represents the global angular spectrum after aggregation of all 28 microphone pairs and the locations of the two detected sources. Thanks to the multichannel setting, the indeterminacies have been resolved and the contrast has improved.

Experimental setting and performance measures

We investigated the capability of SRP-PHAT to return the correct DOA of the speaker and the noise source. Based on the ground truth timestamps, joint speaker and noise localization is performed on the time interval containing the keyword and the sentence. The results are reported separately on average over all "quiet" conditions and all noisy conditions. The speaker DOA and the noise DOA (in noisy conditions) are assumed to be the largest and the second largest peak of the angular spectrum, respectively. In addition, noise-only localization is performed on the first 4 s of the recording which contain noise only. The noise DOA is then assumed to be the largest peak.

The number of detected DOAs is varied from 1 to 8. The results are evaluated in terms of recall, that is the proportion of correct DOAs returned, and localization error in degrees, computed only for those DOAs which are considered as correct. These metrics are computed in three different ways:

• in azimuth only -an estimated DOA is considered as correct if the absolute difference with the ground truth azimuth is less than 10°and the error is the average absolute difference for correct DOAs;

• in elevation only -an estimated DOA is considered as correct if the absolute difference with the ground truth elevation is less than 10°and the error is the average absolute difference for correct DOAs;

• in both azimuth and elevation -an estimated DOA is considered as correct if the angle it forms with the correct DOA is less than 10°, and the error is the average angle between the unit norm vectors pointing in the true and estimated directions11 .

Results

Speaker localization

The performance of speaker localization in quiet and in noise is displayed in Fig. 4. Due to the 3D array geometry, the recall and the error are similar in azimuth and in elevation. In quiet, correct speaker localization is systematically achieved, with 99% recall for one returned DOA and 100% for 2 or more returned DOAs, and the correct DOAs are within 2°of the ground truth. This is enough for subsequent enhancement, e.g., by beamforming. In noise, the recall severely degrades when compared to the performance in quiet but the loss of accuracy (in the order of 0.2°) is insignificant.

Due to thresholding effects (we recall that average errors are computed only on estimated DOAs considered as correct with respect to the 10°tolerance on the considered criterion), the average localization error in total angle apparently doesn't benefit from increasing the number of estimated DOAs, even while azimuth and elevation errors are improved separately. This is of little significance when compared to the precision of ground truth measurements (5°), the resolution of the sphere sampling (1°) and the variance of the errors (about 1.5 to 2°, not shown here.)

The diversity and size of voiceHome-2 allow deeper investigation. As an example, we divided the corpus into two subsets of 720 utterances, depending on whether the speaker distance is below or above 2 m. The differentiated results highlight the impact of the speaker distance. Obviously, the recall in quiet remains very close or equal to 100% irrespective of the distance. However, the recall in noise (see Fig. 5) decreases for larger distances. The average loss of accuracy (not shown here) from small to large distances is insignificant.

Overall, this indicates that the target DOA is generally among the estimated DOAs but it is not always the first one in noisy conditions, especially when the speaker is far from the microphone array and the noise source may be closer or stronger. Possible strategies to retrieve the correct DOA among the returned DOAs could be: i) to perform localization on noise-only intervals (see below) and exclude the resulting noise DOAs, or ii) to classify the corresponding signals to determine which one is the targeted speech.

Noise localization

Besides speaker localization, noise localization is also useful for source separation and speech enhancement. As seen in Fig. 6, this task is harder and requires returning a larger number of DOAs. Joint speaker and noise localization on the same time interval (keyword and sentence) is difficult: the recall in both azimuth and elevation reaches 75% only when 5 or more DOAs are returned. Much better results can be obtained if localization is performed on the first 4 s of each recording, with 75% recall in both azimuth and elevation for 1 DOA returned and 95% recall in either azimuth or elevation with 4 DOAs returned. Thus, in realistic applications, estimating the speaker and noise DOAs on different time intervals would be preferable. In practice, such a strategy could be deployed if: i) the last few seconds of noise before the keyword can be accessed via a buffer, or ii) a voice activity detection module or an event classification module is employed to detect noise-only intervals during the utterance.

We stress here the diversity of the noise conditions encountered in the corpus, which span different levels of difficulty or complexity of the scene. In particular, the recorded sounds include diffuse noise sources, complex noises with several DOAs, and cases where no direct path was observed between the noise source and the microphones.

Speech enhancement and ASR

Baseline Speech Enhancement System

Source separation has proved to be a valuable speech enhancement strategy for subsequent ASR, as evaluated for instance in the CHiME challenges [START_REF] Barker | The PASCAL CHiME speech separation and recognition challenge[END_REF][START_REF] Vincent | The second CHiME speech separation and recognition challenge: An overview of challenge systems and outcomes[END_REF]. We replicated the strategy deployed by [START_REF] Ozerov | Using the FASST source separation toolbox for noise robust speech recognition[END_REF] and adapted it to perform source separation on the voiceHome-2 corpus using the FASST toolbox [START_REF] Salaün | The Flexible Audio Source Separation Toolbox Version 2[END_REF] 12 . FASST is based on local Gaussian modeling in the time-frequency domain. The multichannel covariance of each source in each time-frequency bin is expressed as the product of a spatial term (spatial covariance matrix) and a spectral term (short-term power spectrum), which is itself factored into the product of template spectra and time activation coefficients by means of multichannel nonnegative matrix factorization (NMF).

We consider that there are two sources: speech and noise. First, the single-channel clean data (audio/clean) are used to train speech template spectra by 32-component NMF. This is done by a first call to the FASST toolbox, where the template spectra are initialized by vector quantization of the input magnitude spectrogram. Second, for each utterance to be enhanced, a spatial and spectral model of the noise is trained from the first 4 s of the recording which contain noise only. This is achieved by a second call to FASST, where the spatial covariance matrices are initialized via a rank-1 model computed from the DOA of the noise source. Noise template spectra are learned by 16-component NMF. Finally, actual source separation is performed by a last call to FASST, using the previously trained models and the speaker DOA to initialize its spatial covariance matrix. The template spectra are now kept fixed and only the time activation coefficients and the spatial covariance matrices are adapted to the test signal. Given the template spectra and the estimated time activation coefficients and spatial covariance matrices, separation is achieved by multichannel Wiener filtering.

We implemented several configurations based on this general approach:

• Two time-frequency representations can be used: either a 1024-bin short time Fourier transform (STFT) or an 8-band equivalent rectangular bandwidth (ERB) transform [START_REF] Vincent | Adaptive harmonic spectral decomposition for multiple pitch estimation[END_REF]. These choices respectively correspond to a high-quality configuration with high frequency resolution and a real-time configuration with lower frequency resolution but real-time operating capability.

• The speech template spectra can be either speaker-dependent (only clean data from the test speaker are used for training) or speaker-independent (clean data from the other 11 speakers excluding the test speaker are used for training).

• Either the ground truth speaker and noise DOAs or the estimated DOAs are used for initialization of the spatial covariance matrices of speech and noise. The estimated DOAs are the first DOA returned by SRP-PHAT on the time interval containing the keyword and the sentence (speaker localization) and the first DOA returned on the first 4 s of the recording (noise localization).

• The estimated short-term power spectra of speech and noise, that are the product of the estimated template spectra and time activation coefficients, are smoothed over time using a sliding rectangular window as described by Vincent (2010, eq. ( 8)) before computing the multichannel Wiener filter. The length of the window is set to 1 (no smoothing), 5, 9, or 17 frames. Smoothing helps tuning the tradeoff between noise reduction and speech distortion [START_REF] Vincent | An experimental evaluation of Wiener filter smoothing techniques applied to under-determined audio source separation[END_REF]. By reducing speech distortion (at the cost of increased residual noise), better ASR performance can often be obtained (Virtanen et al., 2012).

This resulted in 32 different configurations in total.

Baseline ASR System

We conducted ASR experiments on noisy and enhanced data to serve as a baseline for further use of the corpus. The ASR baseline was implemented using Kaldi [START_REF] Povey | The Kaldi speech recognition toolkit[END_REF].

The acoustic features are 13 mel frequency cepstral coefficients (MFCCs), which are concatenated with 3 frames of left and right context, reduced to 40 dimensions by linear discriminant analysis (LDA), transformed by feature-space maximum likelihood linear regression (fMLLR), and eventually concatenated with 5 frames of left and right context. The effectiveness of this feature pipeline for distant-microphone ASR was shown by [START_REF] Tachioka | Discriminative methods for noise robust speech recognition: A CHiME challenge benchmark[END_REF]. The acoustic model is a deep neural network (DNN) with 7 hidden layers and 2048 sigmoid units per layer. The DNN outputs represent 4113 hidden Markov model (HMM) states. The parameters are pretrained using restricted Boltzmann machines and updated by backpropagation using crossentropy as the loss function. Early stopping is conducted based on a validation set. The two deterministic grammars (cf. Section 2.1.1) are used as language models.

Training is performed on a subset of the ESTER corpus [START_REF] Galliano | Corpus description of the ESTER evaluation campaign for the rich transcription of French broadcast news[END_REF], which contains 43 h of "clean" broadcast speech randomly split into 90% for training and 10% for validation. A Gaussian mixture based acoustic model (GMM-HMM) is first trained on these clean data to obtain alignments. A simulated multi-condition corpus is then generated by convolving each utterance of ESTER with a room impulse response and adding a noise signal. The room impulse response and the noise signal are randomly chosen from the voiceHome corpus and they are distinct for every utterance, in the limit of the total number of impulse responses and noise signals. The signal-to-noise ratio is set such that the intensity of speech matches that actually recorded in these homes. We recall again that these homes are different from those used in voiceHome-2 and that the speakers are totally disjoint. The DNN acoustic model is trained on this multi-condition corpus using the clean alignments obtained via the GMM-HMM as targets. Note that the model is not retrained on enhanced training data (as suggested by [START_REF] Yoshioka | The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices[END_REF], this should not be detrimental to ASR performance.)

Performance measures

In contrast with popular semi-simulated corpora cited in the introduction, voiceHome-2 consists of real data only. This favors realism over the availability of a ground truth speech signal. As such, the computation of classical source separation metrics such as SDR, SAR, SIR [START_REF] Vincent | Performance measurement in blind audio source separation[END_REF] is excluded here. In line with the target application, we retain the word error rate (WER) as the main performance metric for ASR but also for speech enhancement. The quality of separation is indirectly assessed through the WER improvement it brings when used as a preprocessing step for ASR. The 95% confidence interval on the WERs reported below is in the order of ±0.3% (for the smallest WER on enhanced data) to ±0.4% (for the largest WER).

Results

Without enhancement, our baseline ASR system yields a WER of 2.15% on clean data and 8.15% on noisy, unprocessed data. The results obtained after speech enhancement are presented in Table 3 for STFT based separation and in Table 4 for ERB based separation.

As now well established, despite the considerable progress lately made in ASR thanks to the introduction of DNN-based technologies and multi-condition training, STFT-based source separation still improves the WER compared to processing the original noisy data. The improvement is as large as 43% relative using speaker-dependent models initialized with the true DOAs and 28% relative using speaker-independent models initialized with the estimated DOAs.

In line with the above localization results, the use of the ground truth speech and noise DOAs provides notably better performance than the estimated DOAs. Indeed, the recall in the order of 60% for the first returned speaker DOA in noisy conditions (see Fig. 4) translates into the fact that the initial spatial covariance matrices sometimes point to a noise source instead of the speaker. FASST can partially recover from such bad initialization thanks to the information provided by the pretrained template spectra, but not always. The strategies listed at the end of Section 3 to improve speaker localization are expected to improve the WER too.

Disappointingly, and in contrast with previous preliminary experiments (unpublished), the real-time ERB-based separation configuration does not preserve the WER improvement to some extent compared to the STFT-based configuration. Although the WER decreases compared to noisy speech processing when the true DOAs are used, the accumulation of localization errors and separation results in a WER increase otherwise. This suggests that further research is needed to identify the best tradeoff between quality and computation time, by exploring more finely the range of parameters controlling this tradeoff.

Also, despite previous evidence of its benefit for improving the separation quality [START_REF] Vincent | An experimental evaluation of Wiener filter smoothing techniques applied to under-determined audio source separation[END_REF], temporal smoothing does not improve the WER significantly. One possible explanation, which remains to be investigated, is that the benefit of smoothing was greater for GMM-HMM based ASR than it is for the latest DNN-based technologies.

Conclusion

The voiceHome and voiceHome-2 corpora provide a variety of multichannel noise and room data and distant-microphone speech, in an unprecedented number of realistic domestic environments. These datasets can serve for the development and testing of robust speech processing technology, including speech and noise localization, speech enhancement, and ASR. As real speech data will always be limited in quantity, the real speech data in voiceHome-2 are fundamentally meant to serve as a test set. By contrast, the possibility to mix clean utterances with the room impulse responses and the recorded noises in voiceHome is the key to obtain enough data for efficient multi-condition training. For these reasons, the effort in enriching the first voiceHome corpus was primarily put on the diversity of recorded noisy, reverberant speech, resulting in the new voiceHome-2 corpus presented here, which includes 12 speakers, 12 rooms inside 4 different real homes, and 48 realistic noise conditions.

The acoustic diversity in noises and environments provided in voiceHome-2 represent an additional resource in the current common effort of the community towards more realism in the development and evaluation of robust speech processing techniques. The baseline localization, enhancement, and ASR software provided together with the data can be used as the basis for the development of future technology and the recall, localization error, and WER results presented in this article as a basis for evaluating progress. The experiments presented in this article were conducted on short utterances only, since spontaneous speech data were manually transcribed in parallel to writing the article and the transcriptions were finalized too late to run the experiments. We will however be running localization, enhancement, and ASR experiments on spontaneous speech data in the near future and release the corresponding baselines.

In the future, the evaluation of the latest DNN-based enhancement algorithms on voiceHome-2, and the collection of speech uttered in natural dialog scenarios and in more ecological situations (for instance through a Wizard-of-Oz scheme) should allow pursuing this effort towards realistic speech processing techniques.

Figure 1 :

 1 Figure 1: Microphone array (left) and schematic placement of microphones on the cube when unfolded (right). Microphones are identified by a number and faces of the cube by a letter. The precise positions of the microphones used for the recordings are available in the corpus annotations.

  jk } jk into a smaller set {α (n) i } i in order to reduce computation time; (c) Compute the time differences of arrival {τ (n) i } i between the two microphones corresponding to the AOAs {α (n) i } i ; (d) Compute the generalized cross-correlation with phase transform (GCC-PHAT) Φ n (t, τ (n)

Figure 2 :

 2 Figure 2: Sampling of the DOA sphere and AOA computation for SRP-PHAT.------→ Mn 1 Mn 2 is the vector defined by the considered microphone pair. The DOA (θ j , ϕ k ) and the corresponding AOA α jk for this microphone pair are illustrated for one point on the sphere. The number of sampled azimuths is constant per elevation.

Figure 3 :

 3 Figure 3: Top: 4 examples of local GCC-PHAT angular spectra (2-channel, i.e. 1 microphone pair, each). Bottom: global SRP-PHAT angular spectrum (8-channel, i.e. 28 microphone pairs). All angular spectra are computed on utterance 1295 and pooled over time. All angles (θ, ϕ) are expressed in the same global coordinate system.

Figure 4 :

 4 Figure 4: Speaker localization performance in quiet and in noise.

Figure 5 :

 5 Figure 5: Speaker localization recall in noise as a function of the speaker distance d.

Figure 6 :

 6 Figure 6: Noise localization performance on either noisy speech or noise-only intervals.

Table 2 :

 2 Types of homes, rooms and noises in the voiceHome-2 corpus. Rooms span floor areas from roughly 8 to 30 m . The "quiet" noise condition in each room is omitted.

  between the signals recorded at the two microphones in each time frame t and for each time difference of arrival τ Interpolate it back to the original angle resolution to obtain the local angular spectrum Φ n (t, α Compute the global spectrum Φ(θ j , ϕ k ) by pooling the local angular spectra Φ n (t, α

	(n) i ; (e) (n) jk ).
	3. (n)

Table 3 :

 3 Baseline WER (%) after speech enhancement using a 1024-bin STFT representation.

Table 4 :

 4 Baseline WER (%) after speech enhancement using an 8-band ERB representation.

	Smoothing	Speaker independent Est. DOA True DOA Est. DOA True DOA Speaker dependent
	None	8.65	6.68	8.91	6.83
	5 frames	8.58	6.72	8.61	6.66
	9 frames	8.68	6.96	8.66	6.96
	17 frames	8.32	6.81	8.40	6.84

https://zenodo.org/record/1252143

Pictures and more information on this platform can be consulted at http://www.loustic.net

JSpeech Grammar Format files containing the full grammars are enclosed in the baseline recognition scripts, downloadable from the corpus webpage.

KEF IQ3 120W

8Ω loudspeaker. 8 MP34DT01 Digital MEMS by ST Microelectronics. 9 These data are not included in the corpus, since they are not synchronized with the microphone array recordings.

http://bass-db.gforge.inria.fr/bss_locate/#mbss_locate

The angle is defined as the acos of the dot product between two unit vectors pointing in the estimated and true directions. Note that this criterion is more restrictive than would be a logical "AND" condition from the two previous. Indeed, when the errors in azimuth only and elevation only are close enough to the 10°threshold, azimuth is considered as correct, elevation as correct, but the DOA "in both" is not.

http://bass-db.gforge.inria.fr/fasst/

Acknowledgements

We acknowledge the support of Bpifrance (FUI voiceHome). We also wish to thank eSoftThings and Deltadore for implementing the microphone array prototype, D. Fohr and O. Mella for their help with setting up the clean ESTER baseline, and all the voluntary speakers and those who shared their homes for the recording sessions.

$ This work has received the support of BpiFrance (FUI voiceHome)


Claudio Vitari
email: vitari.iae@univ-paris1.fr

Roxana Ologeanu-Taddei
email: roxana.ologeanu-taddei@umontpellier.fr

The intention to use an Electronic Health Record and its antecedents among three different categories of clinical staff Authors

Keywords: Intention to use, Electronic Health Record, Clinical staff, Survey, Physicians, Paraprofessionals, Administrative personnel

The intention to use an electronic health record and its antecedents among three different categories of clinical staff

Claudio Vitari, Roxana Ologeanu-Taddei

Background

While the implementation, diversity and uses of information technology (IT) have increased in hospitals, previous research has highlighted its low use among physicians and their resistance to it .

Thus the healthcare sector has to deal with the issue of users' acceptance of IT. Acceptance is now considered a mature field in information systems research . The model assumes that two main antecedents-perceived ease of use (PEOU) and perceived usefulness (PU)-influence the intended and actual use of new IT. PEOU is "the degree to which a person believes that using a particular system would be free from effort" ; PU is "the degree to which a person believes that using a particular system would enhance his or her job performance" .

In healthcare, different factors affecting healthcare professionals' acceptance of software applications have been investigated. Unfortunately, inconsistent results have been found , maybe because the different studies focused on different IT and different occupational groups.

Consequently, more studies are needed to investigate these implications for recent technology, such as electronic health records (EHR).

While previous research showed the effect of task characteristics as moderate variable , very little research has been done on IT acceptance for different occupational groups in the health context. Generally, studies select one occupational group or healthcare staff as a whole . These studies fail to acknowledge the diversity in the autonomy, work practices and power of the occupational groups involved in patient care. There are three main professionals groups within hospitals: doctors, paraprofessionals and administrative personnel . The people in each of these three groups have different educational backgrounds and professional cultures that play a key role in shaping their attitudes toward technologies in the workplace. While doctors tend to be more unwilling to change their traditional practice and use new IT, paraprofessionals tend to accept and use them more readily . Moreover, paraprofessionals adopt more favorable attitudes toward new technology than doctors because they are likely to perceive it as an effective tool for facilitating coordination with other healthcare groups, another important role for them in addition to their bedside responsibilities . Some authors consider that doctors, unlike paraprofessionals, may perceive new IT as a threat because they lead to loss of power , challenging professions and driving to a "new professionalism" . Administrative personnel seem to take yet another point of view. In general, they coordinate external and internal activities, for example, doctors' and paraprofessionals' schedules, to optimize healthcare operations . They generally have a favorable attitude toward the adoption and use of new IT in healthcare because it helps them monitor and manage the work of doctors and paraprofessionals .

Given these findings in the existing literature, we aim to measure PEOU and PU for different occupational groups (doctors, paraprofessionals and administrative personnel) in the same hospital setting, relative to adoption of EHR.

We pose the following research question: "To what extent do the different categories of clinical staff (physicians, paraprofessionals and administrative personnel) influence the intention to use an EHR and its antecedents?"

To answer this research question we develop a research model (Figure 1), with the following hypotheses that we empirically tested via a survey.

The measure of intention of use and its antecedents were initially developed for voluntary uses of 4

IT. Nevertheless, based on previous literature intention to use was also measured in mandatory settings , according to the assumption that "even when users perceive system use to be organizationally mandated, usage intentions vary because some users are unwilling to comply with such mandates." . Consequently Venkatesh & Davis defined voluntariness as "the extent to which potential adopters perceive the adoption decision to be non-mandatory". This definition highlights the difficulty of the degree of voluntarism involved because "even when use is required, variability in the quality and intensity of this use is likely to have a significant impact on the realization of the system benefits" . Even in mandatory settings, the extent of system use varies by user and can lead to users' resistance, which is more or less passive , and to workarounds . Thus, the concept of intention of use is relevant because it can assess the willing to really use the system or to find alternative ways as workarounds. . Studies on intention to use and its antecedents in healthcare highlight relationships that differ from those in other sectors. For example, a study on a clinical decision support system showed that the influence of PU on technology acceptance among physicians was significantly supported, but the influence of PEOU was not . In addition, a systematic review of the literature on physician acceptance of IT showed that the PEOU component of the model is consistently related to PU. This finding is consistent with the results of Holden and Karsh who reviewed 16 data sets. Every one of the 16 tests of the relationship between PU and intention to use was significant. While Aggelidis and Chatzoglou and Pai and Huang state that PEOU affects intention to use, Chau and Hu show that PEOU has no significant effect on PU or physicians' perceptions. Some authors suggest that this inconsistency in the results may be explained by the rapid grasp of the importance of usefulness as opposed to usability , and the availability of support staff to deal with the system .

The PEOU-intention relationship seems to be more inconsistent. It is significant in only seven of 13 tests of the sets relating to mixed groups of professionals and paraprofessionals Holden and Karsh analyzed . Nevertheless, we consider that the effect of PEOU on intention is more likely to be positive, given the majority of tests and previous studies in different organizational settings. We 5 Research identified different antecedents of PU and PEOU. Several concepts were tested. Their variety is one reason for the difficulty in comparing the results of different studies. Subjective norms seem to be less significant for physicians because of their professional independence. Given the specificity of physicians and all health professionals, we might consider self-efficacy a pertinent antecedent. Self-efficacy is defined as "one's belief in his or her ability to execute a particular task/job using a computer." . In line with Bandura's self-efficacy theory , authors have shown that individuals with higher self-efficacy are more likely to experience a positive effect than those with lower self-efficacy. Authors identified that computer self-efficacy influences both PEOU and PU , while other results find that self-efficacy has no significant effect on either PU or PEOU. We therefore hypothesize: H4. Self-efficacy is positively related to PEOU. H5. Self-efficacy is positively related to PU.

Prior studies have found that compatibility is an important factor that impacts upon willingness to adopt innovative technology . Generally, the misfit between health IT (including EHR) and work practices is highlighted. At the same time fit is a very challenging objective because of the complexity and variety of clinical processes .

Several authors maintain that creating a fit between health IT and existing work practices requires the initial acknowledgment that the former will change the latter. By customizing and adapting the 6 system to meet specific needs, users will become more open to using it . The fit between clinical work tasks and the design of the technology significantly impacts on the likelihood of acceptance of health IT . We therefore hypothesize: H6. Misfit is negatively related to PU.

Previous research shows contrasting results about the perceived importance of data security in the provision of healthcare on the PU of health IT . Previous studies have insisted that both privacy and the confidentiality of patient data are necessary to ensure the use of health IT . We consider that data security implies both these factors. We therefore hypothesize: H7. Data security is positively related to PU.

Computer anxiety, defined as people's apprehension, or even fear, when they are faced with the possibility of using computers , has also been found to have a significant impact on PEOU in the context of physicians' acceptance of health IT . We therefore hypothesize: H8. Anxiety is positively related to PEOU.

Many studies highlight the importance of trust as a driver of human behavior . Trust in healthcare IT may include having confidence in both the providers and the system . Within the healthcare sector, the strong influence of physicians' trust on PEOU is consistent with earlier studies that show the positive effect of situational normality and structural assurances on PEOU . We therefore hypothesize: H9. Trust is positively related to PEOU. 7

The purpose of this study is to clarify the possible differences, in intention to use an EHR and its antecedents, existing between different staff categories. As described and argued above, this study proposes 9 hypotheses developed based on previous theories and similar empirical studies.

[Figure 1 around here]

Methods

To test our model of Figure 1 we organized a survey based on an online questionnaire that we administered in a French teaching hospital that covers all the clinical specialties and has more than 2500 beds as well as an emergency department. In 2012 the hospital implemented an EHR system according to a "big bang" strategy, aiming to support all departments and specialties within nine months. This EHR incorporates computerized physicians' order entries, medical and nursing observations, laboratory test results, medical prescriptions, operating room process management, and planning and billing management. It is a fully integrated system with a range of different modules, including admission, discharge and transfer, computerized provider order entries, treatment planning, resources and appointment scheduling systems, and a clinical data warehouse (CDW). Results from ancillary subsystems (e.g., laboratories, imaging, and pharmacy) are automatically integrated into the EHR as pdf files.

Our survey measured the clinical staff's perceptions of EHR, using questions derived from a review of previous studies, adapted from different scales: PU , PEOU , misfit , data security , anxiety , selfefficacy , and trust . Each variable was measured using a question and each question was answered using a seven-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree." For a better understanding of the EHR context and local setting, we conducted four 8 15 interviews with the two physicians involved in the EHR evolution and customization. Additional data were collected from internal reports.

The presence of complementary data collection, beyond the needs for this study, limited the possibilities to include whole scales to measure our constructs of interest. We limited our data collection to one item for each variable, keeping our construct narrowly defined in order to make the single-item measures sufficient . This decision was made as a response to the request of the president of the Delegation of Hospital Information (mandated to configure and customize the EHR for the clinical staff), who commanded the questionnaire and who manage its administration. His argument was that the questionnaire needs to be very short; unless, healthcare professionals would not find time to respond to it. Nevertheless, initially, the questionnaire comprised more items for every construct. The questionnaire was sent for pre-test to a panel of 20 volunteers. 11 respondents actively contributed in the pre-test, asking to reduce further the survey because the respondents considered that more items to measure the same construct were redundant. Furthermore, considering the responses, we contextualized the meaning of EHR. As suggested by the pre-test respondents, we decided to use the appellation of the software instead of the generic term "EHR".

The questionnaire was developed and administered online by the Delegation of Hospital Information to the care staff, during one month. One recall was made. A link inviting the clinical staff to respond to this questionnaire was communicated to them by email and invitations were posted on different staff resting rooms' and corridors' walls.

We noted that the intensity of use of the EHR is not a significant issue because each patient's medical examinations, medical history, prescriptions, planning schedule and administrative documents had to be entered in the EHR. Thus, all clinical staff had to post information or use information posted by others in their daily work, eventually in different ways.

To take these differences into account, we grouped respondents into three main categories: [START_REF] Lapointe | A multilevel model of resistance to information technology implementation[END_REF] professionals (all physicians and medical residents in all medical domains); ( 2) paraprofessionals (all carers and nurses); and (3) administrative personnel (all secretarial staff and personal 9 assistants).

The survey was administrated twice, the first time in December 2013 and then in September 2015.

The 21-month lag between the two surveys was decided empirically with the hospital's top management during data collection, following an important change in the version of EHR used and to avoid summer holidays. As well as our survey questions, the questionnaire included sections relating to several hospital management objectives. All the data were provided by mandatory answers to a questionnaire by the employees of the hospital. We followed strict ethical procedures at every stage of the study to ensure data confidentiality and anonymity of the study participants.

We duly explained the purpose of the study to all employees before verbally obtaining their consent to freely participate. All the participants consented to answer anonymously to the questions asked We tested our hypotheses using multiple regressions analyses that included the assessment of: [START_REF] Lapointe | A multilevel model of resistance to information technology implementation[END_REF] the association between intention to use, ease of use and usefulness; (2) the association between usefulness and ease of use, misfit, data security and self-efficacy; (3) the association between ease of use and anxiety, self-efficacy and trust; and (4) the association between intention to use, ease of use, usefulness and control variables (i.e. transverse work, year of questionnaire administration).

The regression models were run separately for the three categories (physicians and residents, carers and nurses, secretaries and assistants) and for all three categories together. Where appropriate, differences were also tested using analysis of variance (ANOVA). 10

Results

From a population of 6,443 clinical employees, we collected 1,741 responses with the first distribution of the questionnaire (27% response rate) and 1,119 with the second distribution (17% response rate). Concerning our three categories of interest: 169 secretaries and assistants responded to the first questionnaire and 151 the second one, on the 547 total secretaries and assistants employed; 759 carers and nurses participated in the first data collection and 504 in the second one, on the 3754 carers and nurses working at the hospital; 574 physicians and residents contributed to the first questionnaire and 286 to the second one on a total population 1427. The overall percentage of missing data in the sample were small (< 7.5%) and no data imputation was carried out (Table 1 and Table 2).

[Table 1 near here] [Table 2 near here] Altogether, usefulness and ease of use are good predictors of the intention to use EHR, explaining 34% of the variance, which can be considered moderate . For physicians and residents, 37% of the variance of their intention to use is explained by usefulness and ease of use. We have similar values for carers and nurses (32%). Also, the beta coefficient of the two independent variables for the whole sample and for physicians and residents, as well as carers and nurses, are a similar size, around 0.55 for usefulness and 0.33 for ease of use.

Conversely, medical secretaries and assistants have different intentions and perceptions. For this category of personnel, ease of use and usefulness explain only 19% of the variance of intention to use. While usefulness has a beta coefficient close to that of the other personnel categories (0.46), the ease of use variable has no statistically significant influence on intention to use.

Concerning the antecedents of usefulness and ease of use, we do not identify relevant differences 11 21 among the different personnel categories. The R-squared of the different independent variables on usefulness is between 0.35 and 0.43, depending on personnel category. The beta coefficients are around 0.29 for ease of use, around 0.13 for self-efficacy, around -0.18 for misfit (the more EHR fits the job, the more it is perceived as useful), and around 0.17 for data security. The R-squared of the different independent variables on ease of use is between 0.25 and 0.33, depending on personnel category. The beta coefficients are around -0.10 for anxiety (the less the EHR makes people anxious, the more the people perceive it as ease of use), around 0.15 for self-efficacy, and around 0.36 for trust (Table 3).

[Table 3 near here] The introduction of control variables in the model helps us to better explain some results and suggest further exploration. We observe substantially stable R-squared across the models and the personnel categories, with few exceptions worth detailing.

Transversal work, i.e. having a job requiring patients' care in several medical departments, is never statistically significant. The year of questionnaire administration is in some cases statistically significant. For physicians and the personnel as a whole, perception of ease of use of the EHR is negatively influenced by the year of administration of the questionnaire. This suggests that the EHR became more complex to use over time, in parallel with the specific initiatives to improve it. These initiatives to improve it could also explain the influence of the year of the questionnaire administration on the intention to use the EHR for the same physicians and the personnel as a whole: the year of the questionnaire administration positively impacts the intention to use the EHR.

The initiatives to improve the EHR made it more difficult to use, but the physicians and the personnel altogether recognized the importance of using it and hence they strengthen their intention to use the EHR.

On the opposite, year of administration did not seem to have any effect on carers, nurses, secretaries 12 23 and assistants, except making not significant the influence of self-efficacy on ease of use for secretaries and assistants.

Finally, for all the variables, the three personnel categories and all categories together have statistically different mean values (Table 4).

[Table 4 near here]

Discussion

Our results enrich previous contrasting results on intention to use and its antecedents for health IT.

We highlight some differences among personnel categories, an aspect that is still largely unclear in the literature.

For the whole personnel together, all the hypotheses are confirmed, as they are for physicians, residents, carers and nurses. Results are different for secretaries and assistants, whose intention to use the EHR is only influenced by perceived usefulness. Moreover, secretaries and assistants could not be influenced by self-efficacy in the development of their perception of ease of use of the EHR, while for the other personnel categories, self-efficacy, anxiety and trust influence the perceived ease of use of the EHR. We see a considerable difference between physicians, residents, carers and nurses, on the one hand, and secretaries and assistants on the other. For this staff category ease of use does not have any influence on intention to use. This result may be explained by the fact that secretaries, unlike physicians and nurses, are administrative employees and, consequently, have more stringent rules and procedures for their work, including work with the EHR, and they have less professional autonomy. Moreover, secretaries have no medical responsibilities related to patient care and their risk of errors is in the form of errors in recording or failing to find relevant information.

While the literature highlights differences between physicians, residents, carers and nurses in the 13 25 acceptance and use of a new IT , we see a certain homogeneity among these categories. The differences are with the third personnel category-secretaries and assistants-as already identified by some researchers .

Comparing our results with previous literature we reaffirm, contrary to earlier results , that for physicians perceived ease of use has a moderate influence on perceived usefulness. Again, we confirm the role of self-efficacy in building perceptions of usefulness , while questioning its role on ease of use perceptions, in the open debate about their effects and the contrasting empirical results .

Another open debate concerns the role data security plays in perceived usefulness. While some previous results affirm that data security has an impact , and others do not , our results demonstrate that data security has a positive influence on the perceived usefulness of EHR.

Notwithstanding the useful results, this analysis has some limitations. The anonymity of the respondents did not allow us to precisely measure the evolution of the perceptions for each single respondent, via a longitudinal study. Moreover, our survey targeted only one hospital for data collection, which limits the generalizability of our findings. Additionally, we restricted the measure of each construct, in the questionnaire, at one single item. The use of full scales, at the place of single items, would have increased the explained variance and opened to additional statistical analysis.

Conclusions

While the existing literature has focused on the acceptability of health IT by an occupational group or by healthcare staff as a whole, our paper investigates the differences between occupational groups within a teaching hospital. More concretely, we measure the extent to which the three largest categories of clinical staff (physicians, paraprofessionals and administrative personnel) have an influence on the intention to use an EHR and its antecedents.

Mobilizing a set of antecedents of intention to use, we propose a model based on nine hypotheses.

27

The hypotheses were tested mainly using multiple regressions analyses, with and without control variables. For the entire personnel, as a whole, all the hypotheses are confirmed. They are also all confirmed for physicians, residents, carers and nurses but not for secretaries and assistants. Unlike the other personnel categories, secretaries' and assistants' perception of the ease of use of EHR does not influence their intention to use it. Moreover, secretaries and assistants could not be influenced by self-efficacy in the development of their perception of the ease of use of EHR. These results may be explained by the fact that secretaries, unlike physicians and nurses, have to follow more stringent rules and procedures for their work, including working with EHR. They have less professional autonomy than healthcare professionals and no medical responsibility.

We suggest that hospital managers have to take these differences among staff categories in a hospital into account when they implement health IT, particularly in light of the interdependence of these staff categories in patient care and the need to involve all categories in the use of the technology.

We hope that our results would contribute to help EHR decision makers and developers to understand the specific motives impacting the use of the EHR for the different staff categories and hence to include these specificities in their development plans. In particular, secretaries and assistants would need special attention, as their antecedents to the intention to use an EHR have a distinct impact in comparison to the impact that the same antecedents have on the other staff categories. In our study, we advance some justifications for these differences, basing on literature and our empirical knowledge. Nonetheless, we consider that future studies should extend our understanding of the origin of these differences, through qualitative and quantitative methods.

List of abbreviations

EHR: Electronic Health Records IT: Information Technology 15 not sig. Legend: β = Beta coefficient; Ad.d R2 = adjusted R-squared; *** = statistically significant p-value lower than 0.001; ** = statistically significant p-value lower than 0.01; * = statistically significant p-value lower than 0.05; not sig.= not significant when the p-value is higher than 0.05.

3 5

 3

  is positively related to intention to use. H2. PEOU is positively related to intention to use. H3. PEOU is positively related to PU.

  by questionnaire. The survey was presented and accepted by the Medical Commission of the Hospital, the Users' Commission and the Executive Board of the Hospital. All the data were collected anonymously. The respondents accepted to answer to the questions asked. No other ethics approval was necessary to be compliant with the French regulation. Between 2013 and 2015 several actions were initiated within the hospital, including training programs for all clinical staff and customization of EHR forms for different medical departments and occupations: the deployment of a new version of the EHR, the development of an e-learning platform, the continuous customization of EHR, the indexing of folders and files in EHR.

Figure 1 :

 1 Figure 1: The theoretical model Detailed legend: Each rectangle is a construct, each arrow is a hypothesis

Table 1 Variables of the survey

 1 The denominator of the % is 3392, the total number of collected questionnaires. The nominator is the N value in the previous column, the total number of responses to the specific question of the survey.

	Variable	Question	Mean SD		Min Max N	%*
		I prefer to not use the
	Intention to not	EMR to care for my pa-
	use	tients, as far as I can	3.39 2.217	1	3139 92.54%
		The EMR is useful to care
	Usefulness	for my patients	4.21 1.728	1	3152 92.92%
		I find easily the data I need
	Ease of use	in the EMR	3.55 1.74	1	3208 94.58%
		The EMR misfits with my
	Misfit	tasks	4.2 2.108	1	3167 93.37%
		The medical records of my
		patients are secured in the
	Data security	EMR	4.04 1.878	1	3142 92.63%
		I feel alone facing the
	Anxiety	EMR	3.75 1.953	1	3166 93.34%
		I have the resources and
		information to use the
		EMR to care for my pa-
	Self-efficacy	tients	4.05	1.8	1	3165 93.31%
	Trust	I have trust in the EMR	3.64 1.867	1	3167 93.37%
	Note: * = Table 1 Variables of the survey

Table 2

 2

			Sample characteristics
			Respondents, for each category, to the question-
				naire administered in:
			Year 2013	Year 2015
				% of re-		% of re-
				spondents		spondents
		Declaring		on the total		on the total
		doing a	Number of	number of	Number of	number of
		transversal	respon-	respon-	respon-	respon-
	Staff categories	work	dents	dents	dents	dents
	Secretaries and as-	Yes	16	1.15%	15	1.59%
	sistants	No	147	10.53%	136	14.45%
	Carers and nurses	Yes No	82 630	5.87% 45.13%	72 432	7.65% 45.91%
	Physicians and resi-	Yes	187	13.40%	113	12.01%
	dents	No	334	23.93%	173	18.38%
		Total	1396 100.00%	941 100.00%
	Table 2 Sample characteristics

Table 3 Results of regression analysis split by staff category

 3

	Without control vari-ables		Ad.d R2 and p-value			β and p-value
	Indepen-dent vari-able	Depen-dent Variable	All sam-ple	Physi-cians and resi-dents	Carers and nurses	Secre-taries and as-sistants	All sam-ple	Physi-cians and resi-dents	Carers and nurses	Secre-taries and as-sistants
	Usefulness Ease of use	Intention to not use	0.34***	0.37***	0.32***	0.19***	-0.55*** -0.31***	-0.55*** -0.38***	-0.54*** -0.29***	-0.46*** not sig.
	Ease of use						0.29***	0.30***	0.27***	0.28***
	Self-effi-cacy Misfit	Useful-ness	0.39***	0.39***	0.35***	0.42***	0.11*** -0.20***	0.08** -0.22***	0.12*** -0.18***	0.17*** -0.14**
	Data secu-rity						0.18***	0.17***	0.18***	0.16***
	Anxiety Self-effi-cacy	Ease of use	0.33***	0.30***	0.33***	0.25***	-0.11*** 0.17***	-0.07* 0.18***	-0.12*** 0.17***	-0.11* 0.12*
	Trust						0.37***	0.35***	0.37***	0.35***
	With control variables		Ad.d R2 and p-value			β and p-value
	Indepen-dent vari-able	Depen-dent Variable	All sam-ple	Physi-cians and resi-dents	Carers and nurses	Secre-taries and as-sistants	All sam-ple	Physi-cians and resi-dents	Carers and nurses	Secre-taries and as-sistants
	Usefulness						-0.54***	-0.54***	-0.54***	-0.44***
	Ease of use Transver-sal work	Intention to not use	0.35***	0.37***	0.32***	0.20***	-0.31*** not sig.	-0.38*** not sig.	-0.29*** not sig.	not sig. not sig.
	question-naire						-0.17*	-0.32*	not sig.	not sig.
	Ease of use						0.29***	0.30***	0.27***	0.28***
	Misfit						-0.20***	-0.22***	-0.17***	-0.14***
	Self-effi-cacy Data secu-rity	Useful-ness	0.39***	0.39***	0.35***	0.43***	0.11*** 0.17***	0.08** 0.18***	0.12** 0.18***	0.15*** 0.16***
	Transver-sal work						not sig.	not sig.	not sig.	not sig.
	question-naire						not sig.	not sig.	not sig.	not sig.
	Anxiety						-0.11***	-0.07*	-0.12***	-0.13*
	Self-effi-cacy						0.17***	0.20***	0.17***	not sig.
	Trust Transver-sal work	Ease of use	0.33***	0.30***	0.33***	0.26***	0.38*** not sig.	0.37*** not sig.	0.38*** not sig.	0.37*** not sig.
	question-naire						-0.23***	-0.35**	not sig.

Table 3

 3 Results of regression analysis split by staff category 23 45

Table 4 Results of the analysis of variance

 4

	Variable				Staff Category
		Physicians and	Carers and	Secretaries and
								All	F (sig.)
		residents	nurses	assistants
		Mean	SD	Mean	SD	Mean	SD	Mean	SD
	Trust	3.34	1.84	3.38	1.86	4.42	1.73	3.50	1.87 44,9***
	Self-efficacy	3.73	1.72	4.02	1.80	4.89	1.62	4.03	1.79 49,4***
	Anxiety	4.07	1.91	3.80	1.97	3.03	1.81	3.80	1.96 33,0***
	Ease of use	3.38	1.68	3.30	1.70	4.30	1.61	3.46	1.72 46,5***
	Misfit	4.52	2.06	4.50	2.11	3.05	1.87	4.32	2.12 69,0***
	Data security	3.86	1.83	3.89	1.94	4.46	1.79	3.95	1.89 13,1***
	Usefulness	4.11	1.68	3.86	1.76	4.93	1.40	4.08	1.72 49,0***
	Intention to
	not use	3.78	2.26	3.66	2.25	2.19	1.60	3.52	2.24 65,4***

Legend: SD= Standard Deviation = F-test of the ANOVA; *** = statistically significant p-value lower than 0,001.

Table 4

 4 Results of the analysis of variance 24 47

Acknowledgements:

We thank Dr David Morquin for the administration of the questionnaire, the useful information provided on the context of the hospital and for technical assistance for editing the manuscript.

Availability of data and materials:

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

PEOU: Perceived Ease Of Use PU: Perceived Usefulness CDW: Clinical Data Warehouse

Declarations

Ethics approval and consent to participate: This study does not include any data on subjects, human material, or human data from patients. All the data were provided by voluntary answers to a questionnaire by the employees of the hospital.

We followed strict ethical procedures at every stage of the study to ensure data confidentiality and anonymity of the study participants. We duly explained the purpose of the study to all employees before verbally obtaining their consent to freely participate. All the participants consented to answer anonymously to the questions asked by questionnaire. The survey was presented and accepted by the Medical Commission of the Hospital, the Users' Commission and the Executive Board of the Hospital. All the data were collected anonymously. The respondents accepted to answer to the questions asked. No other ethics approval was necessary to be compliant with the French regulation.

Consent for publication:

Not applicable

Competing interests:

The authors declare that they have no competing interests Funding:

Funding for this research was provided by the University of Montpellier for the language editing, proofreading and for the article-processing charge.