Development of Automatic Speech Recognition Techniques for Elderly Home Support: Applications and Challenges

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

doi:10.1007/978-3-319-20913-5_32

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…We note that, although high, the word error rate of the elderly participants in the current study is consistent with other studies using automated speech recognition on elderly speech, 42,43 even in controlled laboratory settings. 44 There are reports that pre-processing of elderly speech can decrease the word error rate by up to 12%, 45 although evidence suggests that natural language processing models are relatively impervious to high word error rate.…”

Section: Discussionsupporting

confidence: 92%

Acceptability of collecting speech samples from the elderly via the telephone

et al. 2021

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Objective There is a critical need to develop rapid, inexpensive and easily accessible screening tools for mild cognitive impairment (MCI) and Alzheimer’s disease (AD). We report on the efficacy of collecting speech via the telephone to subsequently develop sensitive metrics that may be used as potential biomarkers by leveraging natural language processing methods. Methods Ninety-one older individuals who were cognitively unimpaired or diagnosed with MCI or AD participated from home in an audio-recorded telephone interview, which included a standard cognitive screening tool, and the collection of speech samples. In this paper we address six questions of interest: (1) Will elderly people agree to participate in a recorded telephone interview? (2) Will they complete it? (3) Will they judge it an acceptable approach? (4) Will the speech that is collected over the telephone be of a good quality? (5) Will the speech be intelligible to human raters? (6) Will transcriptions produced by automated speech recognition accurately reflect the speech produced? Results Participants readily agreed to participate in the telephone interview, completed it in its entirety, and rated the approach as acceptable. Good quality speech was produced for further analyses to be applied, and almost all recorded words were intelligible for human transcription. Not surprisingly, human transcription outperformed off the shelf automated speech recognition software, but further investigation into automated speech recognition shows promise for its usability in future work. Conclusion Our findings demonstrate that collecting speech samples from elderly individuals via the telephone is well tolerated, practical, and inexpensive, and produces good quality data for uses such as natural language processing.

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Section: Discussionsupporting

confidence: 92%

Acceptability of collecting speech samples from the elderly via the telephone

et al. 2021

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“…The assessment results of the first experiments were adopted to draw conclusions about the alarm called 'detection'. The second experiments included participants who had tried the fall scenarios in a practical smart home; 67% of the distress calls were found online [9]. The PaeLife project is a European business-scholar collaboration in the scheme of the Ambient Assisted Living Joint Program (AALJP), which aims to develop a multi-mode, polyglot digital personal life assistant to support elderly citizens who are socially incorporated.…”

Section: Automatic Speech Recognition Techniquesmentioning

confidence: 99%

Mobile Application Development of Managing Elderly Household Accounts Using Speech Recognition

Nuanmeesri

2020

Int. J. Interact. Mob. Technol.

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Using spoken language is the easiest and most natural way of communication for human beings. The communication between humans and machines using sounds or language is a huge move forward. This article demonstrates the development of a mobile application for managing household accounts with the integration of a speaking voice and touch-screen function on smartphones; this could also be used as a tool to manage household accounts for elderly Thai people. The content was evaluated by five experts to identify each feature of the developed mobile application. Afterwards, the developed mobile application was distributed to thirty-three elderly people. The findings showed that the elderly people were able to learn and use the mobile application effectively. The learning post-test results scored higher than the pre-test results. The effectiveness evaluation results in terms of the accessibility barriers that can be faced by elderly people while using mobile applications showed that the average mean was 4.13 and the standard deviation was 0.36 and the acceptance of the mobile application based on accessibility barriers that could be detected by elderly people while being used has high consensus. This indicates that the developed mobile application is effective for elderly users at a high level.

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“…However, repetitions and sentence fragments. These factors, which are particularly relevant in the utterances of elderly people and patients with dementia [37,38], pose challenges to the parsing capabilities of language understanding systems and require specific techniques for robust processing, that range from syntactic/semantic input pre-processing (e.g., with the introduction of additional grammar rules able to recognize and handle some of these phenomena occurring sufficiently regularly) to partial parsing (similar to shallow parsing approaches) when a full linguistic analysis on each input term fails. This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction.…”

Section: Automatic Speech Recognitionmentioning

confidence: 99%

Dialogue Systems and Conversational Agents for Patients with Dementia: The Human–Robot Interaction

Russo

D’Onofrio

Gangemi

et al. 2019

Rejuvenation Research

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This study aimed to identify and describe the fundamental characteristics of spoken dialogue systems, and their role in supporting human-robot interaction and enabling the communication between socially assistive robots and patients with dementia. First, this work provides an overview of spoken dialogue systems by considering the underlying technologies, approaches, methods, and general issues. Then, the analysis focuses on studies, systems, and approaches that have investigated the role of dialogue systems and conversational agents in the interaction with elderly people with dementia by presenting the results of a literature review. While the overview of spoken dialogue systems relies on existing surveys and reviews, a research was conducted to identify existing works in the literature that have investigated the role of conversational agents and dialogue systems in the elderly and people with cognitive impairments. Inclusion criteria were as follows: (1) use of conversational agents, dialogue systems, or language processing tools for people with cognitive impairments; (2) age ≥60 years; (3) diagnosis of dementia according to National Institute on Aging-Alzheimer's Association (NIAAA) criteria; (4) presence of tests or experiments with qualitative or quantitative results. Initially 125 studies published between 2000 and 2017 were identified, of which 12 met the inclusion criteria. The review identifies the issues and challenges that are reported when conversational agents and speech-based interfaces have been used for interacting with people with cognitive impairments. In addition, the review led to the identification of studies that have investigated speech processing and natural language processing capabilities to assess the cognitive status of people with dementia.

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Development of Automatic Speech Recognition Techniques for Elderly Home Support: Applications and Challenges

Cited by 13 publications

References 27 publications

Acceptability of collecting speech samples from the elderly via the telephone

Acceptability of collecting speech samples from the elderly via the telephone

Mobile Application Development of Managing Elderly Household Accounts Using Speech Recognition

Dialogue Systems and Conversational Agents for Patients with Dementia: The Human–Robot Interaction

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