Computer audition for healthcare: A survey on speech analysis

Intelligent speech analysis (ISA) constitutes a significant component within the realm of computer audition (CA) technology. Speech, as a fundamental tool for human communication, not only conveys rich semantic information but also holds significant potential for various healthcare applications. Computational paralinguistics methods can be used to analyse alterations in the acoustic characteristics of speech signals induced by medical conditions, providing valuable insights into shifts in an individual’s health status. More importantly, compared to other physiological monitoring devices, speech acquisition devices are non-invasive and user-friendly, making them accessible for a wide range of individuals. However, despite its promise, ISA in healthcare currently faces a range of notable challenges that hinder its widespread adoption. In this survey, we present an overview of the development and current research in speech analysis technologies within the healthcare domain. First, we summarise the methodologies employed in ISA-based healthcare. Next, we provide an overview of applications in evaluating physical diseases, mental health conditions, and neurological disorders. Additionally, we discuss key limitations and shortcomings in the current state of the field. Finally, we conclude with a summary of the discussed works and offer insights into future research directions aimed at addressing these limitations to advance the practical implementation of ISA in clinical settings. This survey aims to serve as a valuable resource for researchers in speech analysis, biomedicine, and related fields. We hope to inspire greater interest in this promising area within the scientific community and provide guidance for future studies in this evolving field.

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Publication Details

Journal
AI Open
Published
2025-01-01
DOI
https://doi.org/10.1016/j.aiopen.2025.10.001
Citations
1
Primary Topic
Voice and Speech Disorders
Type
article
Field-Weighted Citation Impact
1.16
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article

Computer audition for healthcare: A survey on speech analysis

Kun Qian, Fuze Tian, Yang Tan, Björn W. Schuller et al.
1 citations
AI Open
Voice and Speech Disorders
1.16
article

Computer audition for healthcare: A survey on speech analysis

Kun Qian, Fuze Tian, Yang Tan, Björn W. Schuller, Cuiping Zhu, M.H. Cho, Zhonghao Zhao, Yoshiharu Yamamoto, Weijia Zhang, Bin Hu
article en
1 citations

Abstract

Intelligent speech analysis (ISA) constitutes a significant component within the realm of computer audition (CA) technology. Speech, as a fundamental tool for human communication, not only conveys rich semantic information but also holds significant potential for various healthcare applications. Computational paralinguistics methods can be used to analyse alterations in the acoustic characteristics of speech signals induced by medical conditions, providing valuable insights into shifts in an individual’s health status. More importantly, compared to other physiological monitoring devices, speech acquisition devices are non-invasive and user-friendly, making them accessible for a wide range of individuals. However, despite its promise, ISA in healthcare currently faces a range of notable challenges that hinder its widespread adoption. In this survey, we present an overview of the development and current research in speech analysis technologies within the healthcare domain. First, we summarise the methodologies employed in ISA-based healthcare. Next, we provide an overview of applications in evaluating physical diseases, mental health conditions, and neurological disorders. Additionally, we discuss key limitations and shortcomings in the current state of the field. Finally, we conclude with a summary of the discussed works and offer insights into future research directions aimed at addressing these limitations to advance the practical implementation of ISA in clinical settings. This survey aims to serve as a valuable resource for researchers in speech analysis, biomedicine, and related fields. We hope to inspire greater interest in this promising area within the scientific community and provide guidance for future studies in this evolving field.

AI OpenVol. 6
Beijing Institute of Technology (CN), Japan Graduate School of Education University (JP), Ministry of Education (ME), Imperial College London (GB), Technical University of Munich (DE), The University of Tokyo (JP), Lanzhou University (CN)
Openalex Percentile: Top 17%
Voice and Speech Disorders
1.16
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