Frédéric Aman
email: frederic.aman@imag.fr

Véronique Aubergé
email: veronique.auberge@imag.fr

Michel Vacher
email: michel.vacher@imag.fr

How affects can perturbe the automatic speech recognition of domotic interactions

Keywords: ASR, Smart Home, Vocal Commands, Expressive Speech

In Smart Home, the vocal home automation orders, for comfort purposes, or assistive devoted, have been pointed as the more relevant interaction for ambient assisted living. Even if the orders are very strictly formulated, when they are daily used (directed to the smart home, or to a robot mediator), they become often pronounced with various affects. In this paper we have evaluated how some state of the art ASR systems shut down with expressive orders, acted or spontaneous, and how the ASR training with neutral and/or acted and/or spontaneous expressive commands corpus can greatly modify the ASR performances.

Introduction

Different studies have been shown that the more convenient and acceptable modality for Smart Home users to control the ambient intelligence is the vocal commands. It is mainly relevant for weakened people, like aged people, how can have some motricity and/or visual difficulties [START_REF] Portet | Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects[END_REF]. In such kind of usages, that are nearer to assistive technologies than to comfort (it can be devoted to help aged people to keep life at home), it is usually asked to users to remember quite strictly the commands, that is the exact morpho-syntactico-lexicology of each command. In particular some distress commands are necessarily included to the home automation controls. That is that one main problem of ASR systems, in such context, is supposed to be resistive to the speech signal degradations because of the environment: long distance capture, mixing with home noises, etc. But the commands are supposed to be pronounced quite neutral. However, it can be challenged that in real use, these commands will be expressive in two ways: [START_REF] Callejas | Designing smart home interface for the elderly[END_REF] if the user builds a representation of the intelligent apartment as a communicative entity (HAL paradigm) -it can even be embodied by a mediator robot-that is that the command prosody can implement intentions, attitudes and other interactional socio-affective values, (2) if the command is motivated by pulled emotions. It is thus clear that more the command is related to something important for the user, more the speech signal is expressive. However, if many studies are related on emotions/social affects automatic recognition [START_REF] Schuller | Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge[END_REF], some evaluations on the ASR performances specifically to expressive vs. non expressive natural speech could not be found in the field on automatic spontaneous speech recognition. This paper is devoted to measure how some selected state of the art ASR systems can deal with expressive and non expressive spoken commands, since a daily system must sufficient on non expressive commands, that is for the majority of commands, but must be particularly efficient for expressive commands, of course especially for distress commands.

About domotic vocal commands

Smart Home and Ambient Assisted Living

The evolution of communication technologies has facilitated the emergence of new ways of designing an habitat through the concept of Smart Home. The Smart Home is a residence equipped with computer technology that anticipates and meets the needs of the occupants trying to optimally manage their comfort and safety by pressing the home and implementing connections with the outside world [START_REF] Aldrich | Smart homes: Past, present and future[END_REF]. One of the biggest challenges in Ambient Assisted Living (AAL) is to develop smart homes that anticipate and respond to the needs of their inhabitants, this is especially important when they have disabilities. A special case is that of Smart Homes equipped with specialized devices for the detection of distress and for remote monitoring of the health [START_REF] Chan | A review of smart homes-present state and future challenges[END_REF]. The aim of the project GER'HOME [START_REF] Zouba | A computer system to monitor older adults at home: preliminary results[END_REF] is to design, test and certify technical solutions supporting services to help maintain home elderly. While respecting the criteria of satisfying needs, respect for ethical and legal constraints, economic interest, the proposed systems must provide assistance in three main areas:

1. health (monitoring of health and loss autonomy), 2. safety (prevention and detection of distress), 3. and assistance with home automation (compensation of handicaps by providing easy access to household appliances). There is a fourth aspect, the communication with relatives and outside which is essential for people who are isolated at home. More generally, Smart Homes tend to be equipped with interfaces more complex and much more difficult to control by the user. Those who benefit most from these new technologies are people losing their autonomy, people with motor disabilities or weakened by various diseases [START_REF] Callejas | Designing smart home interface for the elderly[END_REF]. Many proposed systems are primarily concerned with monitoring, they often use sensors placed on the person and which are reserved for cases of severe disability [START_REF] Zappi | Activity recognition from on-body sensors: Accuracy-power trade-off by dynamic sensor selection[END_REF] [3], cameras [START_REF] Aggarwal | Human activity analysis: A review[END_REF] which currently pose problems of acceptability [START_REF] Portet | Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects[END_REF] and ethics [START_REF] Tapia | Activity recognition in the home using simple and ubiquitous sensors[END_REF]. The sensors, door contacts and meters (water, electricity, heating) are widely used. Audio consideration of information is almost absent from all projects except Companionable [START_REF] Milhorat | Hands-free speech-sound interactions at home[END_REF], HERMES [START_REF] Jiang | HERMES: A FP7 funded project towards computer-aided memory management via intelligent computations[END_REF] and CHIL [START_REF] Brdiczka | Detecting human behavior models from multimodal observation in a smart home[END_REF]. Potential users, however, are less able to use complex interfaces because of their disability or lack of familiarity with new technologies. It therefore becomes essential to provide assistance to facilitate everyday life and access to all the socalled "home automation" through Smart Home systems. The usual touch interfaces should be supplemented by more accessible interfaces, seeking neither sight nor movement, thanks to a system reactive for speech. Such systems also find their utility when, even momentarily, the person can hardly move. The sound analysis involves two distinct areas, the automatic speech recognition (voice command and dialogue) and identification of sounds. Many challenges must be overcome before these technologies can be used on a living [START_REF] Vacher | Development of Audio Sensing Technology for Ambient Assisted Living: Applications and Challenges[END_REF], which explains the small number of projects based on sound analysis. Regarding the particular speech, recognition is difficult because of the strong distance between the speaker and the microphone, resulting in a greater influence of reverberation, noise and environmental sounds and cone sensitivity microphone. Moreover, the results obtained with recognition systems are highly degraded in relation to children and the elderly [START_REF] Gerosa | Towards ageindependent acoustic modeling[END_REF]. With age, the floor is prone to earthquakes, imprecise articulation of consonants and slower articulation [START_REF] Ryan | Perceptual and acoustic correlates in the speech of males[END_REF].

Cirdo context

In this context, the CIRDO project (French ANR) promotes autonomy and support for elderly people by caregivers through the social inclusion product e-lio and thanks to the integration of innovative services (automatic speech recognition, analysis of situations or scenes in an uncontrolled environment) to promote independence and support for caregivers, patients with chronic diseases or Alzheimer's disease or related. In addition, this project will allow the validation of generic technologies, ergonomic and a psychological evaluation on the use of services but also critical investigation of the knowledge gained by professionals in human services. One major aim of this project is to detect distress situation of the person living alone at his home through audio and video analysis. In this framework, our objective in the project is to integrate an ASR system that will include detection of distress situations and voice commands. This is a big challenge because this includes the automatic speech recognition of emotional aged voice.

Expressivity in vocal commands

The ASR for natural speech is not a really solved problem, even if the role, that is the sub-speech, is quite known [START_REF] Dufour | Investigation of spontaneous speech characterization applied to Speaker role recognition[END_REF]. If the addressed speech is vocal commands, the main treated problems are phonologico-syntactic (reformulations, fillers, etc). But the prosodic perturbations of speech signals are not really addressed as a problem for ASR performances. On another side, many studies have been devoted to automatic emotion recognition. However, it is surely easier for a user to control a strict lexico-syntactic formulation of the command, than to control the prosodic expressivity, since some emotions are not voluntary expressed. The pragmatic problem in daily applications of home automation vocal commands, is thus how the speech recognition can be efficient both on neutral and expressive vocal commands, and even really robust to recognize the command for very expressive distress commands.

Methodology

In speech recognition, the acoustic models are often trained on large amount of recordings of newspaper readings or journalist speeches. In real situation, speech can be very distant from the read or journalistic speech, particularly when it is spontaneous expressive speech. In this paper, we study the impact of emotions on ASR systems on the acoustic level. We decoded some expressive speeches in order to answer the following questions: when speech data contain emotions, does the ASR system well resist in term of accuracy by comparing with speech without emotion? Does the choice of different modalities of data used for testing and for model adaptation has an influence on the decoding accuracy?

We compared three modalities of speech: -spontaneous expressive speech: the speaker was talking with real emotions, -acted expressive speech: the speaker was talking by acting the emotions, -modal speech: the speaker was talking without any emotion. We tested the decoding on these 3 modalities by using different ASR systems, and with several acoustical models adapted on these different modalities. The metrics of the systems analyzed in our paper are the correct-word rate (CER) and the correct-phoneme rate (CPR).

The corpora

The corpus used in this study is made of spontaneous and acted expressive speech, and of modal speech without emotion. Data of 6 speakers from the E-Wiz corpus [START_REF] Aubergé | E-wiz: A trapper protocol for hunting the expressive speech corpora in lab[END_REF] recorded in 2003 by the GIPSA laboratory were retained for the expressive speech, and we recorded, in 2012, 7 other speakers for the modal speech data.

Acted and spontaneous expressive speech corpus

The stimuli used in this study for expressive speech were extracted from the French expressive corpus E-Wiz [START_REF] Aubergé | E-wiz: A trapper protocol for hunting the expressive speech corpora in lab[END_REF]. This corpus was recorded using the Wizard of Oz technique, in which the subject is convinced to be interacting with a complex person-machine interface while the apparent behavior of the application is remote-controlled by the experimenter. Subjects were recruited with the pretext of participating in the last pre-commercialization tests of a novel voice-recognitionbased language-learning application, presented as acting directly on subjects brain plasticity to enable a fast and easy learning of foreign vowels pronunciation. Most of the tasks consisted in perceptual discrimination of pairs of synthetic vowels, visually presented in the acoustic triangle. The interactions of the subjects with the system were restrained to a command language composed of the French monosyllabic color names "brique" (brick), "jaune" (yellow), "rouge" (red), "sable" (sand) and "vert" (green) and the command "page suivante" (next page), enabling the collection of at least 20 utterances of each stimulus per subject, balanced across the successive phases of the scenario. The performances attributed to the 17 subjects participating in the experiment were manipulated according to a predefined scenario. Subjects perception skills were first presented as among the better observed so far, prior to getting worse and worse. In the last step of the scenario, modified audio stimuli were presented to subjects to induce random choice of answers, while pretending that the learning software might have damaged their perceptual abilities. This scenario enabled the induction of both positive and negative emotions. The affects expressed were annotated by the subjects themselves from the video recording, as a first step before perceptual validation. An adapted protocol was set up for the 7 subjects who were also actors: those subjects were requested immediately after the Wizard of Oz task to express on the same utterances the affects they reported to have felt during the experiment, as well as the most frequently studied emotions (sadness, anger, fear, disgust, surprise and joy), using their acting methods. The experimenters insisted that the actors should express the affects felt in the experiment the same way they had been expressing them before. The actors recruited were practicing improvisation theater and/or street acting. The E-Wiz corpus has been used by [START_REF] Laukka | Graded structure in vocal expression of emotion: What is meant by "prototypical expressions[END_REF], where the authors worked on the graded structure in vocal expression of emotion. They retained the productions of 6 speakers (3 males, 3 females), all actors, and they classified the expressions into 3 categories, which were referred to as "spontaneous", "acted non-prototypical" and "acted prototypical" expressions. Three broad classes of emotion, for which there were a sufficient number of utterances, were selected: anger, fear, and joy. In a pretest, they retained 193 productions of the 6 speakers, and they evaluated these productions by 15 French speaking naive listeners, who were allowed to listen to the stimuli as many times as they wanted and have to select for each one either an emotion class among the 3 proposed (anger, fear, or joy) or an "other emotion" label. Among stimuli identified with accuracy above chance, the authors retained 12 stimuli in each emotion for each category, for a total of 108 stimuli. In our study, we kept the same 108 productions but we merged the categories "acted non-prototypical" and "acted prototypical" into one category that we referred to as "acted". The repartition of the emotions into the categories spontaneous expressive speech and acted expressive speech are given in Tables 2 and3. These two categories can be considered as subcategories of the larger category expressive speech.

Modal speech corpus

To compare expressive speech with speech without expression, we collected data to construct the third category that we called "modal speech". We recorded 7 French speaking subjects (3 males, 4 females) for whom we asked to read the utterances with the least expressive manner possible, by using a recording application displaying the stimuli on a monitor. The stimuli were the same as for the expressive speech with the 5 monosyllabic color names "brique", "jaune", "rouge", "sable", "vert" and the command "page suivante". Each speaker read almost 10 occurrences of each type of stimuli, for a total of 415 utterances as shown in Table 4.

The selected ASR systems

We compared the ASR performance on two decoders: the ASR toolkit Sphinx3 [START_REF] Seymore | The 1997 CMU Sphinx-3 English broadcast news transcription system[END_REF] and the Google speech web service. The Google speech web service is an on-line service providing by the Google's servers. It can be considered as a black box because we have no knowledge of its internal workings. It is very easy to use, but in the other hand we have access only to few basic parameters. The models are very generic, trained on a large amount of Google's data. It is accessible via a POST request at the address: www.google.com/speech-api/v1/recognize. On the contrary, Sphinx3, an open source system, is used locally off-line. We can manage the configuration parameters of the ASR and can train our own models specific to the task.

Google speech server

Google provides a web service of speech recognition for web applications. An example of use of this web service is Google Chrome. Indeed, Google added in Google Chrome 11 the support for the HTML speech input API which allows web developers to implement speech recognition into web pages.

The API calls internally this web service for processing speech to text. The web service requires a recorded audio file that is passed to the Google server via POST request, and the server responds with a JSON object containing the n-best hypothesis and their confidence scores.

Sphinx

With Sphinx3, we used a context-dependent acoustic model with 3-state left-to-right HMM. The acoustic vectors are composed of 13 MFCC coefficients, the delta and the delta delta of each coefficient. This HMM-based context-dependent acoustic model was trained on the BREF120 corpus which is composed of about 100 hours of annotated speech from 120 French speakers. We called it the generic acoustic model.

Acoustic Model Adaptation

The most common method to overcome the ASR limitation is to apply speaker adaptation. Speaker adaptation consists in generating a new acoustic model from a generic one and some new annotated speech in limited quantity. One of the most popular technique is to apply the Maximum Likelihood Linear Regression (MLLR) which is particularly adapted when a limited amount of data per class is available. MLLR is an adaptation technique that uses small amounts of data to train a linear transform which warps the Gaussian means so as to maximize the likelihood of the data. The principle is that acoustically close classes are grouped and transformed together.

Experiments

Google decoding

We launched the decoding with Google speech web service on the three categories spontaneous expressive speech, acted expressive speech and modal speech. We compared the resulting 1-best hypothesis with the reference by using the NIST SCLITE toolkit for computing the CER (Table 5). The correct-word rates are equal to 65.5% for modal speech, 39.3% for acted expressive speech and 30.2% for spontaneous expressive speech. For expressive speech (acted+spontaneous), the CER is equal to 35.9%. We can observe an absolute difference of 29.6% between the modal speech and the expressive speech. That demonstrate the influence of the emotions, degrading the performance of decoding. We can also observe that acted expressive speech is quite better than spontaneous speech, with an absolute difference of 9.1%. Because of mono-word utterances, sometimes Google decoded the word "vert" (means green) by its homophone "verre" (means glass). The percentages of word resulting in homophones vert/verre was 9.5% for modal, 4.5% for acted and 11.3% for spontaneous. If we consider than hypothesis utterances are correct if phonetically identical, the CER are respectively for modal, acted and spontaneous equal to 75.1%, 43.8% and 41.5%.

Sphinx decoding

We separated each speech category into two parts, half for acoustic adaptation, and the other half for testing. We adapted the BREF120 generic acoustic model with the data of the different speech categories.

We obtained 4 new acoustic models: -BREF120 adapted to modal speech -BREF120 adapted to acted expressive speech -BREF120 adapted to spontaneous expressive speech -BREF120 adapted to expressive speech (acted+spontaneous). We realized with Sphinx3 a phonemic decoding by testing the different speech categories with the BREF120 acoustic model and the different adapted acoustic models. The phonemic references for comparing with hypothesis are obtained by forced alignment with Sphinx3. The results are shown in Table 6. We can observe that without adaptation (BREF120 generic acoustic model), the test with modal data gives the best CPR (69.83%), the worst CPR is given by spontaneous expressive speech (46.21%), and for the acted expressive speech, the CPR is 58.37%. The ranking of the speech categories is the same than with the Google decoding. The absolute difference of CPR between modal speech and expressive speech is 16.01%. In all the cases, the acoustic adaptation improves the results. For the decoding of spontaneous speech expressive speech, the using of the acoustic model adapted with spontaneous expressive speech produces the best result (CPR=64.80%) compared to the other adapted models. The recording of modal speech by reading text is obviously easier than recording spontaneous expressive speech, but the adaptation with modal speech gives the worst result (CPR=57.94%). The adaptation with acted expressive speech produces CPR=60.00%, less than the result with spontaneous expressive speech, showing that the properties of acted expressive speech are different than the properties of spontaneous expressive speech. If the models are not adapted with the same category of data that the category of data used for testing, the decoding with the model adapted to expressive speech permits to increase the results for all the speech categories. For the decoding of acted expressive speech, the best model is the one adapted with expressive speech (CPR=67.46%). When testing with modal speech, the less efficient model is the model adapted to acted expressive speech (CPR=77.64%). Finally, when decoding with expressive speech, the best model is the model adapted with expressive speech (CPR=65.57%) and the worst is the model adapted with modal speech (CPR=63.24%).

Conclusion

In this study, we addressed the pragmatic problem of how some ASR systems, efficient for neutral commands -that are not perturbed by morpho-syntactic reformulations, neither fillers -can deal in real-life home automation commands, when the utterances are mixed within expressive and non expressive commands. This problem cannot be related with the automatic recognition of emotions, since the lexical recognition of commands is a sufficient issue, even for distress commands (the distress information is largely contained by the lexico-morpho-syntactic recognition). We could observe, but mixing all the combinations of expressive/non expressive corpus for the systems training, and the systems running, that there are many variations of ASR performances depending on how are trained the system, whatever the ASR tested system. That means that for real applications in home automation commands, especially for weakened users, this problem must absolutely be taken into account, and the ASR systems must be specifically adapted. The question is opened to understand if the ASR could be helped by the emotion automatic recognition processing (that are not directly required).

Acknowledgement

This study was funded by the National Agency for Research under the project CIRDO -Industrial Research (ANR-2010-TECS-012). The authors would like to thank the volunteer subjects who accepted to perform this experimentation and give their time to do so correctly.

Table 1 :

 1 Number of speakers by data category.

	Data category	Gender and number
	Acted and spontaneous	3 females
	expressive speech	3 males
	Modal speech	4 females 3 males
	Total	13

Table 2 :

 2 Number of utterances by type of stimuli for acted category.

	Utterance	Anger Fear Joy Total
	brique	3	1	2	6
	jaune	6	5	8	19
	rouge	4	3	5	12
	sable	4	6	0	10
	vert	4	4	3	8
	page suivante	6	5	6	17
	Total	24	24	24 72

Table 3 :

 3 Number of utterances by type of stimuli for authentic category.

	Utterance	Anger Fear Joy Total
	brique	1	3	0	4
	jaune	1	1	1	3
	rouge	0	2	1	3
	sable	1	0	0	1
	vert	1	5	2	8
	page suivante	8	1	8	17
	Total	12	12	12 36

Table 4 :

 4 Number of utterances by type of stimuli for modal category.

	Utterance	Modal
	brique	70
	jaune	70
	rouge	70
	sable	69
	vert	69
	page suivante 67
	Total	415

Table 5 :

 5 Correct-word rate, number of words in reference, number of correct words in hypothesis, substitutions, deletions, insertions and homophones vert/verre, by category modal speech, acted expressive speech and spontaneous expressive speech.

	Cat.	CER	Word Corr. Sub. Del. Ins. H.
	modal 65.5% 482	316	163 3	13 46
	acted	39.3% 89	35	51	3	4	4
	spont. 30.2% 53	16	33	4	3	6

Table 6 :

 6 Correct-phoneme rate (CPR) for the phonemic decoding of modal speech, acted expressive speech, spontaneous expressive speech and expressive speech for the acoustic models: BREF120, adapted to modal speech, adapted to acted expressive speech, adapted to spontaneous expressive speech, adapted to expressive speech.

	AM/Test modal acted spont. emotions
	BREF120 69.83% 58.37% 46.21% 53.82%
	modal	81.90% 66.36% 57.94% 63.24%
	acted	77.64% 66.35% 60.00% 63.99%
	spont.	79.48% 65.24% 64.80% 65.07%
	emotions 80.30% 67.46% 62.40% 65.57%


Frédéric Aman
email: frederic.aman@imag.fr

Michel Vacher
email: michel.vacher@imag.fr

Solange Rossato
email: solange.rossato@imag.fr

François Portet
email: francois.portet@imag.fr

Speech Recognition of Aged Voice in the AAL Context: Detection of Distress Sentences

Keywords: Speech recognition, AAL, Dependence, Elderly, Keyword detection I

By 2050, about a third of the French population will be over 65. In the context of technologies development aiming at helping aged people to live independently at home, the CIRDO project aims at implementing an ASR system into a social inclusion product designed for elderly people in order to detect distress situations. Speech recognition systems present higher word error rate when speech is uttered by elderly speakers compared to when non-aged voice is considered. Two specialized corpora in French, AD80 and ERES38, were recorded in this framework by aged people, they were used first to study the possibility of adaptation of standard ASR to aged voice. Then we looked at whether the variability of the WER between speakers could be correlated with the level of dependence. Then, we assessed the performance of distress sentence detection by a filter and we demonstrated a significant drop in performance for those with the lowest degree of autonomy.

INTRODUCTION

Life expectancy has increased in all countries of the European Union in the last decade. In the beginning of 2013, France has 17.5% of its citizens of at least 65 years old. Furthermore, 9% of the people in France are at least 75 years old. The increase of life expectancy and the ageing of baby boomers are the main factors of ageing [START_REF] Bellamy | Bilan démographique 2012. la population croît, mais plus modérément[END_REF]. The number of dependent elderly people will increase in the coming years, with 50% more of dependent people by 2040 according to a projection conducted by the INSEE French institute [START_REF] Duée | La dépendance des personnes âgées : une projection en 2040[END_REF]. At the end of 2010, 12 million people in France received the Personalized Allocation of Autonomy (APA), where 61% of them were at home and 39% were in specialized institutions [START_REF] Bellamy | Bilan démographique 2012. la population croît, mais plus modérément[END_REF]. A survey shows that 80% of people above 65 years old would prefer to stay living at home if they lose autonomy [START_REF] Csa | Les français et la dépendance[END_REF]. The notion of dependency is based on the alteration of physical, sensory and cognitive functions, on the restriction of the activities of daily living, and on the need for help or assistance. Therefore, ageing can cause functional limitations that -if not compensated by technical assistance or environmental management -lead to activities restriction. Then, the person needs the assistance of someone for regular elementary activities (nursing home services, domestic help, etc.) [START_REF] Tlili | Perspectives démographique et financières de la dépendance[END_REF]. Some technological solutions based on robotics, automation, cognitive science, and computer networks have been developed to compensate physical or mental decline, and to provide assistance if necessary through surveillance by detecting distress and accidental falls. Some systems also facilitate social contacts, helping caregivers as well as reassuring relatives. However, elderly are often confused by complex interfaces in technological systems. Therefore, the usual interfaces (remote controls, mice or keyboards) must be complemented by more accessible and natural interfaces such as a system of Automatic Speech Recognition (ASR).

In this context, the CIRDO project 1 promotes autonomy and support for elderly people by caregivers through the social inclusion product e-lio 2 and thanks to the integration of innovative services (automatic speech recognition, analysis of situations or scenes in an uncontrolled environment) to promote independence and support for caregivers, patients with chronic diseases or Alzheimer's disease or related. In addition, this project will allow the validation of generic technologies, ergonomic and a psychological evaluation on the use of services but also critical investigation of the knowledge gained by professionals in human services.

The use of elderly voice can be an issue for performance of speech recognition system. Indeed, ageing voice is characterized by some specific features such as imprecise production of consonants, tremors and slower articulation [START_REF] Ryan | Perceptual and acoustic correlates in the speech of males[END_REF]. From the anatomical point of view, some studies have shown age-related degeneration with atrophy of vocal cords, calcification of laryngeal cartilages, and changes in muscles of larynx [START_REF] Takeda | Aging effects on motor units in the human thyroarytenoid muscle[END_REF], [START_REF] Mueller | Acoustic and morphologic study of the senescent voice[END_REF]. Some authors [START_REF] Baba | Acoustic models of the elderly for large-vocabulary continuous speech recognition[END_REF], [START_REF] Vipperla | Longitudinal study of ASR performance on ageing voices[END_REF] have reported that classical ASR systems exhibit poor performances with elderly voice because most acoustic models of ASR systems are acquired from non-aged voice samples. Privat et al. [START_REF] Privat | Etude de l'effet du vieillissement sur les productions langagières et sur les performances en reconnaissance automatique de la parole[END_REF] studied the usability of a system of dictation in French for elderly vs. young people in various situations of vocal production, showing that certain classes of phonemes such as the front and rounded vowels were badly recognized. These few studies were relevant for their comparison between ageing voice vs. non-ageing voice on ASR performance, but their fields were quite far from our topic of automation commands recognition, and no study was done in French language.

Another issue for our work was the non-existence of a speech corpus in French containing distress utterances and automation commands.

This paper presents the ASR ability to detect sentences of distress in the case of ageing voice. In Section II, we present some projects and studies related to distress detection in the Ambient Assisted Living (AAL) context with a special attention to audio analysis used in this framework. The audio processing system CirdoX is presented in Section III.A and the proposed method of detection of emergency calls in the home is described in Section III.B. The specialized corpora that we recorded and the ASR system are described in Section IV, then we show the necessity to adapt acoustic models to the aged voice in Section V and present findings of the adaptation in the same section. In Section VI, we introduce the problem of the variability of performances of the detection of emergency call for the elderly people case before discussing the results of distress sentences filtering for the different levels of elderly dependence. Section VII is related to the conclusion of this study.

II. RELATED WORKS

In the Ambient Assisted Living context, a Health Smart Home is a habitation equipped with a set of sensors, actuators and automated devices to provide ambient intelligence for daily living task support, early detection of distress situations, remote monitoring and promotion of safety and well-being [START_REF] Portet | Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects[END_REF]. A large number of research projects have contributed to the field, among them House_n [START_REF] Intille | Designing a home of the future[END_REF], Casas [START_REF] Chen | Behavior-based home energy prediction[END_REF], ISpace [START_REF] Holmes | The iDorm: Gateway to heterogeneous networking environments[END_REF], Aging in Place [START_REF] Skubic | A smart home application to eldercare: Current status and lessons learned[END_REF], Ger'Home [START_REF] Zouba | A computer system to monitor older adults at home: Preliminary results[END_REF] or Soprano [START_REF] Wolf | Soprano -an extensible, open aal platform for elderly people based on semantical contracts[END_REF]. The main trends of these projects are related to distress detection, health status monitoring and cognitive stimulation; a great variety of sensors are used like wearable video cameras, embedded sensors, medical sensors, switches and infrared detectors.

Microphones and audio technologies are less taken into consideration. Regarding security in the home, speech technologies can help a person in danger to call for help from anywhere in a multi-room home without having to use a touch interface that can be out of reach. For instance, the Sweet-Home project [START_REF] Vacher | Recognition of voice commands by multisource ASR and noise cancellation in a smart home environment[END_REF] takes advantage of a smart home equipped with home automation through sensors, microphones and actuators driven by an intelligent controller allowing in context interaction and detection of distress situations. The Reside and DesdHIS projects studied the detection and classification of sounds in order to send an alarm in presence of cry or glass breaking [START_REF] Istrate | Information extraction from sound for medical telemonitoring[END_REF]. Other projects considered the fall detection using a wearable microphone which is often fused with other modalities like motion thanks to the use of an accelerometer [START_REF] Popescu | An acoustic fall detector system that uses sound height information to reduce the false alarm rate[END_REF], [START_REF] Doukas | Enabling human status awareness in assistive environments based on advanced sound and motion data classification[END_REF]. Doukas et al. proposed a patient awareness system to detect body fall and vocal stress in speech expression through analysis of motion data and acoustic, but in this case the person is constrained to wear sensors continuously [START_REF] Doukas | Enabling human status awareness in assistive environments based on advanced sound and motion data classification[END_REF]. Another approach proposed a dialog system in order to replace traditional emergency systems and fit the life style of the elderly [START_REF] Hamill | Development of an automated speech recognition interface for personal emergency response systems[END_REF]. However, the vocabulary of the prototype was limited (yes or no) and the system was not tested with aged users. In [START_REF] Filho | From science fiction to science fact: a smarthouse interface using speech technology and a photo-realistic avatar[END_REF], a communicative avatar was designed to interact with a person in a smart office. This study takes advantage of beam forming for speech enhancement and for a geometric area of recording but was not tested in a multi-room realistic home. Another popular approach is the use of companion robots like in the CompanionAble project [START_REF] Milhorat | Hands-free speechsound interactions at home[END_REF]. The companion robot is able to move nearby the person and then the user is close to the avatar. The system is able to recognize commands uttered in Dutch, no specific application to distress sentence recognition were reported by the authors. This system is limited by the capacity of the robot to move close to the person.

To the best of our knowledge, the main focus of these projects is the detection of the distress of the person but doesn't include the detection of distress sentences and calls uttered by aged voices. Indeed, in the CIRDO project, the aim is to enable detection of distress situation and call for help at home. In this perspective the project addresses the important issues of aged voice recognition and distress sentence detection.

III. DISTRESS SENTENCE DETECTION

We developed an application for the recognition of distress situations by analyzing the audio signal to detect sound and voice. This work is planned to be coupled with an analysis of video scenes developed by the LIRIS laboratory in Lyon.

A. Audio processing in line: CirdoX

Fig. 1 shows in the first place the processing chain and processing of the audio signal, and then its interaction with video analysis and the data fusion system in charge of interaction with the social inclusion device e-lio. The method used in the last step for detection of emergency calls is presented in Section B.

CirdoX is made of two parallel processes communicating by IPC (Inter-Process Communication) signals. This application is designed to be modular, with independent modules so that each module can be chosen from among several plug-ins corresponding to different techniques.

1) First process

The audio stream is captured by a microphone and recorded by the Acquisition module at the input of "Process 1". This module can work through the plug-ins using the PortAudio library (audio card), Kinect or a National Instrument card. Then, the Detection module detects the occurrence of a signal (sound or voice). The detection of the occurrence of an audio event is based on the change of energy level of the three highest frequency coefficients of the Discrete Wavelet Transform (DTW) in a floating window frame (last 2048 samples without overlapping). Each time the energy on a channel goes beyond a self-adaptive threshold, an audio event is detected until the energy decrease below this level for at least an imposed duration [START_REF] Istrate | Information extraction from sound for medical telemonitoring[END_REF]. Then, a wave file is created by the Wave Creation module.

2) Second process

This output is then analyzed by "Process 2". The Discrimination module determines whether the signal is sound or speech. This module can be executed through the plug-ins using a Gaussian Mixture Model (GMM) or decision tree. In the case of sound, the Classification module defines to which sound class the signal belongs. It can work through GMM, decision tree and also Hidden Markov Model (HMM). In the case of speech, the signal is sent to the ASR module in order to output a sentence. Both hypotheses from Classification and ASR module are received by the Data Formatting module that sends a socket containing all the relevant data to the Fusion module of the global system.

3

) Fusion system

Finally, in addition to audio information presented before, the Fusion module also receives data from the recognition of distress by video analysis in order to make a fusion between video and audio data. This one determines whether or not a distress situation is occurring and sends a socket containing its decision to the social inclusion product, e-lio (see Fig. 2). Then, e-lio executes an appropriate action, such as calling a relative, a physician or a carer.

B. Detection of emergency calls in the home

As a preliminary approach, we used a filter to detect the distress sentences into the ASR hypotheses by using Levenshtein distance [START_REF] Levenshtein | Binary codes capable of correcting deletions, insertions and reversals[END_REF]. This distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertion, deletion, substitution) required to change one word into the other. In our case, we use it to determine whether a sentence recognized by the ASR is close to a distress sentence by calculating the difference between the reference sequence which is the correct transcription of such sentence, and the hypothesis which is the recognized sentence.

Based on the GRePS laboratory's work [START_REF] Bobillier-Chaumon | Démarche de développement de technologies ambiantes pour le maintien à domicile des personnes dépendantes : vers une triangulation des méthodes et des approches[END_REF] that interviewed elderly people in nursing homes to identify and describe what situations of distress they could have experienced, we created a list of sentences that can occur during a distress situation.

The filter calculates the Levenshtein distance between the ASR output hypothesis and all the distress sentences of the list. The sentence from the list with the best Levenshtein distance is selected according to a threshold. The lower the distance (score from 0 to infinity), the better the matching will be. To not be biased by orthography, the distance is calculated on a phonemic level. This approach takes into account some recognition errors such as word endings errors or slight variations. Moreover, in many cases, a miss-decoded word is phonetically close to the correct one due to close pronunciation.

IV. CORPORA AND ASR SYSTEM

Very few corpora are related to ageing voices in French. These different corpora are stem of projects related to the study of French language like the "Corpus de Français Parlé Parisien des années 2000" 3 . This corpus is made of recordings of inhabitants of different districts of Paris in order to study the influence of French spoken language over France and the French speaking world. The "Projet Phonologie du Français Contemporain" 4 is a database of records according to the region or the country. The records of 38 ageing persons (above 70 years old) are included, each record is made of a word list, a small text and two interviews.

Other available sources come from videos of testimonies of Shoah survivals and recorded in the framework of "Mémorial de la Shoah" 5 which collects testimonies and organizes conferences. These videos are not annotated. This corpus is then a collection of interviews and spontaneous speech.

Therefore, an important issue for our work was the nonexistence of speech corpus containing distress calls and home automation commands. Thus, we recorded the French AD80 and ERES38 corpora (see Fig. 3). To enable comparison of system performance between elderly and non-elderly, the corpus contains recordings made by these two groups of people.

A. AD80 corpus

After a bibliographical study in collaboration with the GRePS laboratory and in the prolongation of older studies [START_REF] Vacher | Speech and sound use in a remote monitoring system for health care[END_REF], we defined a list of home automation orders and of distress calls that the person could utter during a distress situation to request for assistance. This list was completed with casual sentences. At the beginning of the CIRDO project, a survey was conducted by the GRePS laboratory [START_REF] Bobillier-Chaumon | Démarche de développement de technologies ambiantes pour le maintien à domicile des personnes dépendantes : vers une triangulation des méthodes et des approches[END_REF] in order to determine what sort of sentences were truly uttered by aged people in distress case and during a fall. The collected sentences were added to the list. Ten samples of each sentence category (distress, home automation order, casual) are given in TABLE I. The non-aged part of the corpus was previously recorded in our laboratory in 2004 and was complemented in 2013 with sentences based on [START_REF] Bobillier-Chaumon | Démarche de développement de technologies ambiantes pour le maintien à domicile des personnes dépendantes : vers une triangulation des méthodes et des approches[END_REF]. The collection of the aged part of the AD80 corpus was performed sporadically from 2009 to 2012 in collaboration with a rehabilitation centre, volunteers and a nursing home. Targeted speakers were persons aged of more than 60 years old, able to read and with no mental disorder or pathology altering the voice. The recording was done with a single microphone positioned at about 30 cm from the speaker's mouth. Most speakers were sat, but some were in a wheelchair or laying in a bed. The recording was done using a computer and a homemade software to prompt sentences to be read by the speaker and to record the utterances using voice activity detection. Given the targeted application (in-home voice commands and distress calls) the participants were requested to read a list of short distress/home automation and casual sentences such as Aidez-moi (Help me) or Il fait beau (It's sunny).

Indeed, the AD80 corpus was acquired from 95 speakers (36 men and 59 women) who were asked to read short sentences as mentioned before. The speakers are divided into two groups: the elderly group composed of 43 speakers (11 men and 32 women), 62 to 94 years old, with 2796 distress and home automation sentences for a duration of 1 hour 5 minutes, and 3006 casual sentences for a duration of 1 hour 6 minutes, and the non-elderly group composed of 52 speakers, 18 to 64 years old, with 3903 distress and home automation sentences for a duration of 1 hour 18 minutes, and 3897 casual sentences for a duration of 1 hour 12 minutes.

Speakers around 60-69 years old were placed in the groups according to their level of autonomy loss, their physical ageing, and whether or not they were in a nursing house. For the 43 speakers of the aged AD80 corpus, a GIR score was obtained after clinicians filled the AGGIR grid (French national test). This score is computed from the performances of the person to classify it in one of the six groups: GIR 1 (total dependence) to GIR 6 (total autonomy).

Finally, the AD80 corpus is made up of 13,602 annotated sentences, with 4 hours and 42 minutes of recording.

B. The training ERES38 corpus

Another aged corpus, the ERES38 corpus (Entretiens RESidences 38: Entretiens means interviews) was acquired for model adaptation purpose. Contrary to AD80, this is a collection of spontaneous speech and the annotation task was an important effort. This corpus was recorded in 2011 in the living place of the person. During the interviews, we requested each speaker to read a text but they were also asked to talk freely about their life. The text was an article about gardening created by the experimenters in order to target phoneme issues reported in [START_REF] Privat | Etude de l'effet du vieillissement sur les productions langagières et sur les performances en reconnaissance automatique de la parole[END_REF], [START_REF] Aman | Contribution à l'étude de la variabilité de la voix des personnes âgées en reconnaissance automatique de la parole (assessment of the acoustic models performance in the ageing voice case for ASR system adaptation)[END_REF]. The ERES38 corpus was acquired from 22 elderly people (14 women and 8 men) between 68 and 98 years old. The corpus included 48 minutes of read speeches and 17 hours of interviews. The speakers lived in specialized institutes, such as nursing homes and were cognitively intact without severe disabilities.

C. ASR

The ASR toolkit chosen in our study was Sphinx3 [START_REF] Seymore | The 1997 CMU Sphinx-3 English broadcast news transcription system[END_REF]. This decoder used a context-dependent acoustic model with 3state left-to-right HMM. The acoustic vectors are composed of 13 MFCC coefficients, the delta and the delta delta of each coefficient. This HMM-based context-dependent acoustic model was trained on the BREF120 corpus [START_REF] Lamel | BREF, a large vocabulary spoken corpus for french[END_REF] which is composed of about 100 hours of annotated speech from 120 non-elderly French speakers (different from AD80). We called it the generic acoustic model.

A general language model (LM) was estimated from French newswire collected in the Gigaword corpus. It was 1gram with 11,018 words. Moreover, to reduce the linguistic variability, a 3-gram domain language model was learnt from the sentences used during the corpus collection described in Section A, with 88 1-gram, 193 2-gram and 223 3-gram models. Finally, the language model was a 3-gram-type which results from the combination of the general language model (with a 10% weight) and the domain one (with 90% weight). This combination has been shown as leading to the best WER for domain specific application [START_REF] Lecouteux | Distant Speech Recognition in a Smart Home: Comparison of Several Multisource ASRs in Realistic Conditions[END_REF]. The interest of such combination is to bias the recognition towards the domain LM but when the speaker deviates from the domain, the general LM makes it possible to correctly recognize the utterances.

V. ASR PERFORMANCES ISSUED WITH AGED VOICES

The experiments carried out in automatic speech recognition have shown performance degradation for "atypical" population such as children or elderly people [START_REF] Wilpon | A study of speech recognition for children and the elderly[END_REF], [START_REF] Vipperla | Longitudinal study of ASR performance on ageing voices[END_REF], [START_REF] Gerosa | Towards age-independent acoustic modeling[END_REF] and have shown the interests of an adaptation to the target populations [START_REF] Gerosa | Towards age-independent acoustic modeling[END_REF], [START_REF] Anderson | Recognition of elderly speech and voice-driven document retrieval[END_REF]. A study of Gorham-Rowan and Laures-Gore [START_REF] Gorham-Rowan | Acoustic-perceptual correlates of voice quality in elderly men and women[END_REF] highlights the effects of ageing on the speech utterance and the consequences on the speech recognition. Speech recognition adapted to the voice of elderly people is still an under-explored area. The relevant languages are mainly English [START_REF] Vipperla | Longitudinal study of ASR performance on ageing voices[END_REF] and Asian languages such as Japanese [START_REF] Baba | Acoustic models of the elderly for large-vocabulary continuous speech recognition[END_REF]. A very interesting study [START_REF] Vipperla | Longitudinal study of ASR performance on ageing voices[END_REF] used some recordings of speeches delivered in the Supreme Court of the United States over a decade. These recordings are particularly interesting because they were used to study the evolution of recognition performance on the same person depending on his age over 7-8 years. A limitation of this study is that it relates to people with good diction and with experience in public speaking. These studies show that the performance of recognition systems decreases steadily with age, and that a special adaptation to each speaker can get closer to the scores obtained from the youngest speakers without adaptation. The implicit consequence is that the recognition system is adapted to a single speaker. To make the system adapted to the person, Renouard et al. [START_REF] Renouard | Independent Living for Persons with Disabilities[END_REF] proposed to use the recognized words to adapt online the recognition models. Proposed in the context of home assistance, this research does not appear to have been pursued. It can be emphasized that no study has considered French aged voice in smart home condition. Moreover, most studies considered the chronological age as global explanatory factor while many other effects can also be responsible for ASR performance degradation as raised by [START_REF] Pellegrini | Impact of Age in ASR for the Elderly: Preliminary Experiments in European Portuguese[END_REF]. There is thus no certainty that age can predict the reliability of a voicebased control system. That is why our study includes an evaluation from the dependence perspective.

A. General acoustic models are unadapted to aged voice

When performing ASR using the generic acoustic model on the distress/home automation sentences of the AD80 corpus, we obtained an average WER of 9% for the nonelderly group, and an average WER of 43.5% for the elderly group, reflecting a significant decrease in performance for aged speech. Fig. 4 represents the WER according to the age of each speaker. Although the corpus contains few speakers between 35 and 60 years old, we can observe that performance decreases steadily with age as shown on the previous studies [START_REF] Baba | Acoustic models of the elderly for large-vocabulary continuous speech recognition[END_REF], [START_REF] Vipperla | Longitudinal study of ASR performance on ageing voices[END_REF], [START_REF] Pellegrini | Impact of Age in ASR for the Elderly: Preliminary Experiments in European Portuguese[END_REF], and that the variability of performance is very significant from the age of 60 years old. For instance, some 83 years old speakers have their WER ranging from 13.6% to 80.2%. Standard deviation is 6% for the non-elderly group and 17.3% for the elderly group. In other words, the WER is far less predictable in the elderly group than in the non-elderly group. Consequently, we have to deal with the fact that speech recognition with such a system can work very well with some of the elderly speakers, and very badly with others.

B. Influence of dependence on ASR performances

The Maximum Likelihood Linear Regression (MLLR) was used to adapt globally the generic acoustic model to the voice of elderly people [START_REF] Aman | Analysing the performance of automatic speech recognition for ageing voice: Does it correlate with dependency level?[END_REF] with the records of ERES38 and we obtained an elderly acoustic model. With the global MLLR adaptation using ERES38, the average WER was 14.52%. Compared to the 43.47% WER without adaptation, the absolute difference was -28.95%.

Among the elderly group and with this acoustic adaptation, we observed a great disparity between the WER for older people in the same age category, this confirms the results of Section A. Therefore, we studied other criteria and focused on the elderly dependency.

TABLE II shows the repartition of the elderly group of the AD80 corpus as a function of the GIR grid, it can be observed that none of them are in total dependence (GIR1) and 35% are in total autonomy (GIR6). Due to the small number of speakers in GIR2, GIR3 and GIR5, we merged GIR2 with GIR3 and GIR4 with GIR5 in order to obtain more balanced classes. The ASR was operated using elderly acoustic model on the distress/home automation sentences of the AD80 corpus. Fig. 5 shows the variation of the WER as a function of the age and for the 3 considered GIR groups.

There is an important dispersion of the results inside each group, the WER averages for GIR2-3, GIR4-5 and GIR6 are respectively 25.2%, 13.2% and 12.2%, and the WER standard deviations are respectively 16.8%, 8.4% and 7.6%. The dispersion is the highest for the most dependent people (GIR2-3), for 50% of this group the WER is equal or upper to 20% (see TABLE III), so that only half of the group obtains average performance. This suggests that the grid is too general to give a reliable indication. Therefore in addition, we conducted a preliminary study on the correlation between WER and each of the 17 parameters of the AGGIR grid. It seems that the parameters characteristic of motor control of the upper limbs and continence may be more correlated with WER. Indeed, these parameters could be representative of an advanced general physical degradation that also affects the voice control and thus diminishes the performance of ASR system

VI. DISTRESS DETECTION RESULTS

Many sentences are recognized by the ASR, but only two categories of them are relevant to the system and must be taken into account: home automation controls and calls related to a distress situation. In addition, users do not want the sentences they utter when they call their relatives are treated by the system [START_REF] Portet | Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects[END_REF]. It is then necessary to determine whether the hypothesis output by the ASR between is part of one of the two categories of interest. Therefore, we use a distance between the test sentence and typical phrases characteristics of these two categories, which allows to exclude phrases of everyday conversation.

A list was generated to categorize the recognized sentences, this list was made of home automation orders and distress calls of the AD80 corpus as described in Section IV-A. A detected sentence occurs when the Levenshtein distance (normalized by the number of phonemes) between the ASR output hypothesis and a sentence from the list is under the threshold. If a distress sentence is detected, it is considered as true positive (TP), otherwise a casual sentence which is under the threshold is considered as a false positive (FP). A casual sentence above threshold is true negative (TN), and a distress sentence above threshold is false negative (FN).

In order to assess the Levenshtein distance filter, we realized a decoding with the elderly speakers from the AD80 corpus, including 2796 distress sentences and 3006 casual sentences, for a total duration of 2 hours 12 minutes. The casual utterances were used as disrupters, with some sentences far from the distress ones: for instance Les patates sont cuites (Potatoes are cooked), or closer, for instance Le médecin a appelé} (The doctor called).

The average WER with the adapted acoustic model was 14.5% for the distress sentences, and was much higher for the casual sentences, 87.5%, due to the adapted-to-distress language model. Then, we drew a ROC curve (Fig. 6) representing the True Positive Rate (TPR) as a function of the False Positive Rate (FPR) by varying the threshold on the Levenshtein distance, for the 43 elderly speakers. At the point of equal error (cutoff = 0.7), sensibility and specificity were equal to 88.4% and 87.6%. We obtained the positive and negative test showed in TABLE IV with threshold = 0.7. Also, we found a recall, precision and F-measure equal to 88.4%, 86.9% and 87.2%. This is quite similar with another study conducted by Lecouteux et al. [START_REF] Lecouteux | Distant speech recognition in a smart home: Comparison of several multisource ASRs in realistic conditions[END_REF], where they detected home automation orders among casual utterances with a multisource ASR in a smart home environment. On their baseline system (best SNR channel), they found WER, recall, precision, and F-measure respectively equal to 18.3%, 88.0%, 90.5% and 89.2%.

In TABLE V, we evaluated the distress sentences detection for the different levels of elderly dependence GIR2-3, GIR4-5 and GIR6 by using recall, precision and F-measure. Once more, we observed that the system performance is quite depending on the level of the elderly dependence. Due to the lower F-measure found on GIR2-3 comparing to GIR4-5 and GIR6 (-9%), we suggest that such a system could be provided to elderly and usable by them only if their autonomy score ranges from GIR4 to GIR6.

VII. CONCLUSION

In this paper, we showed that our ASR system presents higher WER for elderly voice than for young voice, with an average WER equal to 43.5% for the aged group without acoustical adaptation vs. 9% for the non-aged group, for elderly the WER is 14.5% after adaptation using the ERES38 corpus. We observed that the WER is related to the level of dependence of elderly people, with WER = 25.2% for GIR 2-3, WER = 13.2% for GIR 4-5 and WER = 12.2% for GIR 6. We presented the CirdoX software, used to filter and detect the distress sentences. We evaluated this filter based on Levenshtein distance and showed that the recall and precision reach 88.4% and 86.9%. Moreover, we showed that such a system cannot detect distress sentences well enough in the case of elderly in GIR 2-3 because of the high WER in this group.

In a future work, the whole system CirdoX including sound classification, speech recognition and fusion with video scene recognition is going to be evaluated in realistic condition in a real smart home [START_REF] Gallissot | Une plate-forme usage pour l'intégration de l'informatique ambiante dans l'habitat. L'appartement Domus[END_REF]. Professional actors would play some scenarios, including for example falls to the ground because of the foot grasped in the carpet, sudden weakness, etc. We will assess how video can improve distress detection.

Fig. 1 .

 1 Fig. 1. Organisation of CirdoX and its insertion into the global system.

Fig. 2 .

 2 Fig. 2. The e-lio solution by Technosens.

Fig. 3 .

 3 Fig. 3. The recorded corpora AD80 and ERES38.

  3 http://ed268.univ-paris3.fr/syled/ressources/Corpus-Parole-Paris-PIII 4 http://www.projet-pfc.net 5 http://www.memorialdelashoah.org This aged and non-aged corpus is called the AD80 corpus (Anodin-Détresse: anodin means colloquial and détresse means distress).

Fig. 4 .

 4 Fig. 4. WER as a function of age for aged and non-aged groups.

Fig. 5 .

 5 Fig. 5. WER as a function of age and dependency levels.

Fig. 6 .

 6 Fig. 6. ROC curve representing TPR as a function of FPR.

TABLE I .

 I EXAMPLES OF SENTENCES OF THE AD80 CORPUS.

	Sample	Distress Sentence	Home Automation Order	Casual Sentence
	1	Qu'est-ce qui m'arrive !	Appelle quelqu'un e-lio !	Bonjour madame !
	2	Oh là !	e-lio appelle quelqu'un !	Ça va très bien.
	3	Oh là ! Je saigne ! Je me suis blessé !	e-lio appelle les secours !	Ce livre est intéressant.
	4	Aïe ! J'ai mal !	e-lio appelle ma fille !	Il fait soleil.
	5	Je peux pas me relever !	e-lio appelle un docteur !	J'ai ouvert la porte.
	6	Aidez-moi !	e-lio appelle le SAMU !	Je dois prendre mon médicament !
	7	Au secours !	e-lio appelle les pompiers !	J'allume la lumière !
	8	Je me sens mal !	e-lio appelle une ambulance !	Je me suis endormi tout de suite !
	9	Je suis tombé !	e-lio tu peux téléphoner au SAMU ?	Le café est brûlant !
	10	Du secours s'il vous plaît !	e-lio tu peux appeler une ambulance ?	Où sont mes lunettes ?

TABLE II

 II

	.	COMPOSITION OF THE AGED PART OF THE AD80 CORPUS
			ACCORDING TO THE GIR GRID.
	AD80	GIR1	GIR2	GIR3	GIR4	GIR5	GIR6	Total
	Members	0	4	2	21	1	15	43
			GIR2-3	GIR4-5	GIR6
			6		22		15

TABLE III .

 III WER UPPER THAN 20% FOR EACH GIR GROUP.

TABLE IV .

 IV POSITIVE AND NEGATIVE TEST.

	Threshold = 0.7	Distress	Casual
	d ≤ threshold	TP = 2472	FP = 374
	d > threshold	FN = 324	TN = 2632

TABLE V

 V

	.	DISTRESS SENTENCES DETECTION IN FUNCTION OF
			DEPENDENCE.
		Recall	Precision	F-measure
	GIR 6	89.8%	88.2%	89.0%
	GIR 4-5	89.5%	87.2%	88.3%
	GIR 2-3	78.9%	80.4%	79.7%

http://liris.cnrs.fr/cirdo

http://www.technosens.fr

ACKNOWLEDGMENT

This study was funded by the National Agency for Research under the project CIRDO -Industrial Research (ANR-2010-TECS-012). The authors would like to thank to Mrs Vézignol and Bonnefond-Jimenez, Mr Debrus, Mrs Aman, Bron, Lalande and Martins of the medical institutions SSR "Les Cadières" and EHPAD "Château de Labahou" for their help in corpus recording.

Special thanks to R. Dugheanu, J. Le Grand and Y. Sasa for their active contribution, and to various elderly and caregivers who agreed to participate in the recordings.