TY - JOUR
T1 - Computer audition for healthcare
T2 - A survey on speech analysis
AU - Qian, Kun
AU - Zhao, Zhonghao
AU - Tan, Yang
AU - Zhang, Weijia
AU - Cho, Min Ki
AU - Zhu, Cuiping
AU - Tian, Fuze
AU - Hu, Bin
AU - Yamamoto, Yoshiharu
AU - Schuller, Björn W.
N1 - Publisher Copyright:
The Authors
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Computer audition
KW - Deep learning
KW - Intelligent medicine
KW - Intelligent speech analysis
KW - Machine learning
KW - Non-invasive healthcare
KW - Speech
UR - https://www.scopus.com/pages/publications/105024720184
U2 - 10.1016/j.aiopen.2025.10.001
DO - 10.1016/j.aiopen.2025.10.001
M3 - Review article
AN - SCOPUS:105024720184
SN - 2666-6510
JO - AI Open
JF - AI Open
ER -