Development of an automated speech recognition interface for personal emergency response systems

Hamill, Melinda; Young, Vicky; Boger, Jennifer; Mihailidis, Alex

doi:10.1186/1743-0003-6-26

Cited by 57 publications

(48 citation statements)

References 25 publications

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“…It is strongly preferred over a tactile system (or touchscreen) which would necessitate being physically available at the place the command is to be found or would imply to constantly know where the remote controller is. This is in line with other studies concerning personal emergency response systems which showed that push-button based control is not adapted to this population 23 .…”

Section: Results Of the Studysupporting

confidence: 78%

Speech-Based Interaction in an AAL-Context

Vacher¹,

Portet²,

Rossato³

et al. 2012

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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PurposeThe number of older persons in industrialized countries is steadily increasing. Seniors living alone are more numerous, and we must find solutions that will allow them to continue to stay at home comfortably and safely. Smart housings can be one of these solutions. One of the biggest challenges in ambient assisted living (AAL) is to develop smart homes that anticipate and respond to the needs of the inhabitants. Given the diverse profiles of the older adult population, it will therefore be essential to facilitate interaction with the smart home through systems that respond naturally to voice commands rather than using tactile interfaces. Method The first step in our study was to evaluate how well ambient assistive speech technology is received by the target population. We report on a user evaluation assessing acceptance and fear of this new technology. The experiment aimed at testing three important aspects of speech interaction: voice command, communication with the outside world, home automation system interrupting a person's activity. Participants were 7 older persons (71-88 years old), 7 relatives and 3 professional carers; the experiments were conducted in a smart home with a voice command using a Wizard-of-Oz technique. The second step in our study was related to the adaptation of speech recognition technologies to the older adult population. Judging by the literature this has not been extensively studied. In fact, it is known that industrialized speech recognition system models are not adapted to seniors but to other categories of the population. In order to do this we recorded a specific speech corpus (voice-age) with 7 older adults (70 to 89 years old) reading sentences (a total of 4 hours of speech). A second corpus (ERES38) of free talking (18 hours of speech) was recorded by 23 speakers (68-98 years old). These corpora were analyzed in a semi -automatic manner to reveal the aged-voice characteristics. Results & Discussion Regarding the technical aspect, it appears that some phonemes are more affected by age than others. Thus, a specific adaptation of the acoustic models for ASR is required. Regarding the acceptance aspect, voice interfaces appear to have a great potential to ease daily living for older adults and frail persons and would be better accepted than other, more intrusive, solutions. By considering still healthy and independent older persons in the user evaluation, one interesting finding was overall acceptance provided the system is not conducive to a lazy lifestyle by taking control of everything. This particular concern must be addressed in the development of smart homes that support daily living by stressing the ability to control the daily routine rather than altering it. This study shows the great interest of voice interfaces to develop efficient solution to enable the growing number of older persons to continue to live in their own homes as long as possible.

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Section: Results Of the Studysupporting

confidence: 78%

Speech-Based Interaction in an AAL-Context

Vacher¹,

Portet²,

Rossato³

et al. 2012

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Developing an effective fall-alert notifying system enables more seniors to live in the communities with higher autonomy and privacy, and minimizes the impact of fall-resulted injuries. 25 Normal aging is associated with increased reaction time, decreased accommodation and dark adaptation, and weakened muscle power, which are all associative factors of falls. 26 In this study, sensory impairments, such as visual or hearing impairments were not risk factors of falls.…”

Section: Discussionmentioning

confidence: 99%

Living alone as a red flag sign of falls among older people in rural Taiwan

Lee

Cheng

Liu

et al. 2011

Journal of Clinical Gerontology and Geriatrics

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a b s t r a c tBackground/Purpose: Fall is a common geriatric syndrome and is associated with physical injuries, fear of falling, limitation of activities of daily living, and increased medical expenditure. Although risk factors of falls have been explored extensively, little is known about the impact of solitary living on falls in the elderly. The main purpose of this study was to evaluate the impact of solitary living on risk of fall among older people who visited the outpatient geriatric services in rural Taiwan. Methods: From September 2009 to August 2010, all patients who visited the primary careebased outpatient geriatric services were invited for study. For all participants, comprehensive geriatric assessment was performed by a well-trained research nurse. Common risk factors of falls were collected in addition to solitary living, such as polypharmacy (more than four drugs), visual impairment, hearing impairment, age, gender, and cognitive impairment. Results: Overall, 173 (mean age ¼ 78.8 AE 6.8 years, men ¼ 73.4%) patients were enrolled, and 42 (24.2%) of them reported falls in the past year. Two-thirds of fall events occurred outdoors and mainly (66.7%) during walking. Two-thirds of falls were associated with injuries, and half of the patients reported reduced activities of daily living because of fear of falling. Compared with nonfallers, fallers were significantly more likely to live alone (28.6% vs. 14%, p ¼ 0.031), but age (79.0 AE 7.2 years vs. 78.8 AE 6.8 years, p ¼ 0.89); gender (66.7% vs. 76.7%, p ¼ 0.197); polypharmacy (47.6% vs. 39.8%, p ¼ 0.378); use of psycotropic agents (4.8% vs. 0.8%, p ¼ 0.096); visual impairment (35.7% vs. 35.7%, p ¼ 0.995); hearing impairment (21.4% vs. 23.4%, p ¼ 0.79); and cognitive impairment (21.4% vs. 12.5%, p ¼ 0.158) were not statistically significant. Conclusions: In rural Taiwan, solitary living is an important risk factor of falls among patients who visited primary careebased outpatient geriatric services. Further prospective study is needed to evaluate the impact of solitary living on risk of falls, and improvement of outdoor environment is also of great importance to reduce fall-related injuries.

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“…Despite the fact that audio technology has a great potential to become one of the major interaction modalities in Smart Home, this modality is seldom taken into consideration [5][6] [7] [8]. The most important reason is that audio technology as not reached a sufficient stage of maturity and that there is still some challenges to overcome [3].…”

Section: Background and State Of The Artmentioning

confidence: 99%

On Distant Speech Recognition for Home Automation

2015

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Abstract. In the framework of Ambient Assisted Living, home automation may be a solution for helping elderly people living alone at home. This study is part of the Sweet-Home project which aims at developing a new home automation system based on voice command to improve support and well-being of people in loss of autonomy. The goal of the study is vocal order recognition with a focus on two aspects: distance speech recognition and sentence spotting. Several ASR techniques were evaluated on a realistic corpus acquired in a 4-room flat equipped with microphones set in the ceiling. This distant speech French corpus was recorded with 21 speakers who acted scenarios of activities of daily living. Techniques acting at the decoding stage, such as our novel approach called Driven Decoding Algorithm (DDA), gave better speech recognition results than the baseline and other approaches. This solution which uses the two best SNR channels and a priori knowledge (voice commands and distress sentences) has demonstrated an increase in recognition rate without introducing false alarms. Generally speaking, a short overview allows then to outline the research challenges that speech technologies must take up for Ambient Assisted Living and Augmentative and Alternative Communication, and the current reseach avenues in this domain.

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Development of an automated speech recognition interface for personal emergency response systems

Cited by 57 publications

References 25 publications

Speech-Based Interaction in an AAL-Context

Speech-Based Interaction in an AAL-Context

Living alone as a red flag sign of falls among older people in rural Taiwan

On Distant Speech Recognition for Home Automation

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