Smart homes equipped with ambient intelligence technology constitute a promising direction to enable the growing number of elderly to continue to live in their own home as long as possible. However, this calls for technological solutions that suit their specific needs and capabilities. The Sweet-Home project aims at developing a new user friendly technology for home automation based on voice command. This paper reports a user evaluation assessing the acceptance and fear of this new technology. Eight healthy persons between 71 and 88 years old, 7 relatives (child, grandchild or friend) and 3 professional carers participated in a user evaluation. During about 45 min, the persons were questioned in co-discovery in the Domus smart home alternating between interview and wizard of Oz periods followed by a debriefing. The experience aimed at testing four important aspects of the project: voice command, communication with the outside world, domotics system interrupting a person's activity, and electronic agenda. Voice interface appeared to have a great potential to ease daily living for elderly and frail persons and would be better accepted than more intrusive solutions. By considering still healthy and independent elderly people in the user evaluation, an interesting finding that came up is their overall acceptance provided the system does not drive them to a lazy lifestyle by taking control of everything. This particular fear must be addressed for the development of smart homes that support daily living by giving them more ability to control rather than putting them away from the daily routine.

https://hal.archives-ouvertes.fr/hal-00953242/document

Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects

Despite their growing presence in home computer applications and various telephony services, commercial automatic speech recognition technologies are still not easily employed by everyone; especially individuals with speech disorders. In addition, relatively little research has been conducted on automatic speech recognition performance with older adults, in whom speech disorders are commonly present. As one ages, the older adult voice naturally begins to resemble some aspects of mildly dysarthric speech. Dysarthria, a common neuromotor speech disorder, is particularly useful for exploring performance limitations of automatic speech recognizers owing to its wide range of speech expression. This article reviews clinical research literature examining the use of commercial speech-to-text automatic speech recognition technology by individuals with dysarthria. The main factors limiting automatic speech recognition performance with dysarthric speakers are highlighted and extended to the elderly using a ...

Difficulties in automatic speech recognition of dysarthric speakers and implications for speech-based applications used by the elderly: a literature review.

One of the greatest challenges in Ambient Assisted Living is to design health smart homes that anticipate the needs of its inhabitant while maintaining their safety and comfort. It is thus essential to ease the interaction with the smart home through systems that naturally react to voice command using microphones rather than tactile interfaces. However, efficient audio analysis in such noisy environment is a challenging task. In this paper, a real-time audio analysis system, the AuditHIS system, devoted to audio analysis in smart home environment is presented. AuditHIS has been tested thought three experiments carried out in a smart home that are detailed. The results show the difficulty of the task and serve as basis to discuss the stakes and the challenges of this promising technology in the domain of AAL.

https://hal.archives-ouvertes.fr/hal-00757407/document

Development of Audio Sensing Technology for Ambient Assisted Living: Applications and Challenges

Demands on long-term-care facilities are predicted to increase at an unprecedented rate as the baby boomer generation reaches retirement age. Aging-in-place (i.e. aging at home) is the desire of most seniors and is also a good option to reduce the burden on an over-stretched long-term-care system. Personal Emergency Response Systems (PERSs) help enable older adults to age-in-place by providing them with immediate access to emergency assistance. Traditionally they operate with push-button activators that connect the occupant via speaker-phone to a live emergency call-centre operator. If occupants do not wear the push button or cannot access the button, then the system is useless in the event of a fall or emergency. Additionally, a false alarm or failure to check-in at a regular interval will trigger a connection to a live operator, which can be unwanted and intrusive to the occupant. This paper describes the development and testing of an automated, hands-free, dialogue-based PERS prototype. The prototype system was built using a ceiling mounted microphone array, an open-source automatic speech recognition engine, and a 'yes' and 'no' response dialog modelled after an existing call-centre protocol. Testing compared a single microphone versus a microphone array with nine adults in both noisy and quiet conditions. Dialogue testing was completed with four adults. The microphone array demonstrated improvement over the single microphone. In all cases, dialog testing resulted in the system reaching the correct decision about the kind of assistance the user was requesting. Further testing is required with elderly voices and under different noise conditions to ensure the appropriateness of the technology. Future developments include integration of the system with an emergency detection method as well as communication enhancement using features such as barge-in capability. The use of an automated dialog-based PERS has the potential to provide users with more autonomy in decisions regarding their own health and more privacy in their own home.

https://tspace.library.utoronto.ca/bitstream/1807/76774/1/Development%20of%20an%20automated.pdf

Development of an automated speech recognition interface for Personal Emergency Response Systems.

This chapter presents the AUDITHIS system which performs real-time sound analysis from eight microphone channels in Health Smart Home associated to the autonomous speech analyzer RAPHAEL. The evaluation of AUDITHIS and RAPHAEL in different settings showed that audio modality is very promising to acquire information that are not available through other classical sensors. Audio processing is also the most natural way for a human to interact with his environment. Thus, this approach particularly fits Health Smart Homes that include home automation (e.g., voice command) or other high level interactions (e.g., dialogue). The originality of the work is also to include sounds of daily living as indicators to distinguish distress from normal situations. First development gave acceptable results for the sound recognition (72% correct classification) and we are working on the reduction of missed-alarm rate to improve performance in the near future. Although the current system suffers a number of limitations and that we raised numerous challenges that need to be addressed, the pair AUDITHIS and RAPHAEL is, to the best of our knowledge, one of the first serious attempts to build a real-time system that consider sound and speech analysis for ambient assisted living. This work also includes several evalu- ations on data acquired from volunteers in a real health smart home condition. Further work will include refinement of the acoustic models to adapt the speech recognition to the aged population as well as connexion to home automation systems.

/pdf/complete-sound-and-speech-recognition-system-for-health-23e6a8lyom.pdf

Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living

Widespread use of large-vocabulary continuous speech recognition systems has recently occurred, encouraging the application of speech recognition techniques to various problems. One of the factors that adversely affect the performance of speech recognition systems is a mismatch between the acoustic properties of the speech of the system user and the acoustic model. The speech of young or middle-aged adults is generally used in constructing the acoustic model. Thus, a mismatch occurs between the model and the acoustic properties of the speech of the elderly, which may degrade the recognition rate. In this study, a large-scale elderly speech database (200 sentences ×301 subjects) is used to train the acoustic model, and the resulting elderly acoustic model is evaluated by using a large-vocabulary continuous speech recognition system. In the experiments, the word recognition rate was improved by 3 to 5% compared to the recognition results of an acoustic model trained by young or middle-aged adult speech, namely, by the JNAS speech database (150 sentences ×260 subjects, average 28.6 years). It is also verified experimentally that the recognition rate is further improved in speaker adaptation to elderly speech by making use of an acoustic model trained by elderly speech. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 87(7): 49–57, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20101

Acoustic models of the elderly for large‐vocabulary continuous speech recognition

Describes an efficient method for unsupervised speaker adaptation. This method is based on (1) selecting a subset of speakers who are acoustically close to a test speaker, and (2) calculating adapted model parameters according to the previously stored sufficient HMM statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are required for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers' data, a quick adaptation can be done. Compared with a pre-clustering method, the proposed method can obtain a more optimal speaker cluster because the clustering result is determined according to test speaker's data on-line. Experimental results show that the proposed method attains better improvement than MLLR from the speaker independent model. Moreover the proposed method utilizes only one unsupervised sentence utterance, while MLLR usually utilizes more than ten supervised sentence utterances.

Unsupervised speaker adaptation based on sufficient HMM statistics of selected speakers

EUROSPEECH2001: the 7th European Conference on Speech Communication and Technology, September 3-7, 2001, Aalborg, Denmark.

Elderly Acoustic Model for Large Vocabulary Continuous Speech Recognition

This paper describes an efficient method of unsupervised speaker adaptation. This method is based on (1) selecting a subset of speakers who are acoustically close to a test speaker, and (2) calculating adapted model parameters according to the previously stored sufficient statistics of the selected speakers’ data. In this method, only a few unsupervised test speaker’s data are necessary for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers’ data, a quick adaptation can be done. Compared with a pre-clustering method, the proposed method can obtain a more optimal cluster because the clustering result is determined according to test speaker’s data on-line. Experimental results show that the proposed method attains better improvement than MLLR from the speaker-independent model. The proposed method is evaluated in details and discussed.

Evaluation on Unsupervised Speaker Adaptation Based on Sufficient HMM Statictics of Selected Speakers

ICSLP2002: the 7th International Conference on Spoken Language Processing , September 16-20, 2002, Denver, Colorado, USA.

Akira Baba

Papers

Acoustic models of the elderly for large‐vocabulary continuous speech recognition

Unsupervised speaker adaptation based on sufficient HMM statistics of selected speakers

Elderly Acoustic Model for Large Vocabulary Continuous Speech Recognition

Evaluation on Unsupervised Speaker Adaptation Based on Sufficient HMM Statictics of Selected Speakers

Spectral Subtraction in Noisy Environments Applied to Speaker Adaptation Based on HMM Sufficient Statistics