Speech recognition of aged voice in the AAL context: Detection of distress sentences

Aman, Frédéric; Vacher, Michel; Rossato, Solange; Portet, François

doi:10.1109/sped.2013.6682669

Cited by 15 publications

(17 citation statements)

References 19 publications

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“…The acoustic models were trained on about 80 hours of annotated French speech. If the participants were elderly people, the use of adapted data would be required [26], but this was not the case for this study. Given the targeted application of Sweet-Home the computation time should not be a breach of real-time use.…”

Section: The Speeral Asr Systemmentioning

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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Section: The Speeral Asr Systemmentioning

confidence: 99%

On Distant Speech Recognition for Home Automation

2015

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“…Besides the application in health, automated distress detection has also been studied in the Aged Care and Forensic Sciences sectors. In homes for elderly people, distress calls arise if there is a fall or a fire or other such events (Aman, Vacher, Rossato, & Portet, ). In the forensic scenario, automated distress assists the Police to prioritise the crime response based on the intensity of distress of the caller (Aihio, ).…”

Section: Automatic Distress Assessmentmentioning

confidence: 99%

“…For such situations, speaker adaptation techniques or training ASR model on elderly voice can help improvement in recognition rate (Baba, Yoshizawa, Yamada, Lee, & Shikano, ). To explore the performance of ASR in distress situation, Aman et al () presented word error rate in aged voice compared to non‐aged speech. They showed that ASR system gives higher word error rate equal to 43.5% for the aged group and 9% on young speakers.…”

Section: Automatic Distress Assessmentmentioning

confidence: 99%

“…In homes for elderly people, distress calls arise if there is a fall or a fire or other such events (Aman, Vacher, Rossato, & Portet, 2013c).…”

Section: Automati C D Is Tre Ss a Ss E Ss Mentmentioning

confidence: 99%

“…This system involves video and audio analysis to detect the risky situation and make necessary emergency call using e-lio system. For distress detection, CIRDO evaluated the proposed system using automatic speech recognition (ASR) to detect distress sentences in AS80 (Aman et al 2013c) corpus and achieved promising results. The sweet-home project (Vacher, Lecouteux, & Portet, 2012) used home equipped noise robust multisource ASR to detect vocal command or distress sentences in the realistic noisy environment of a smart home.…”

Section: Aged Carementioning

confidence: 99%

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Automated screening for distress: A perspective for the future

et al. 2019

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Distress is a complex condition, which affects a significant percentage of cancer patients and may lead to depression, anxiety, sadness, suicide and other forms of psychological morbidity. Compelling evidence supports screening for distress as a means of facilitating early intervention and subsequent improvements in psychological well‐being and overall quality of life. Nevertheless, despite the existence of evidence‐based and easily administered screening tools, for example, the Distress Thermometer, routine screening for distress is yet to achieve widespread implementation. Efforts are intensifying to utilise innovative, cost‐effective methods now available through emerging technologies in the informatics and computational arenas.

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Wireless Sensor Networks for Aging-in-Place: Theory and Practice

Normie¹

2016

Handbook of Smart Homes, Health Care and Well-Being

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Speech recognition of aged voice in the AAL context: Detection of distress sentences

Cited by 15 publications

References 19 publications

On Distant Speech Recognition for Home Automation

On Distant Speech Recognition for Home Automation

Automated screening for distress: A perspective for the future

Wireless Sensor Networks for Aging-in-Place: Theory and Practice

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