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Automatic speech recognition for acoustical analysis and assessment of cantonese pathological voice and speech
Open accessAbstract
This paper describes the application of state-of-the-art automatic speech recognition (ASR) systems to objective assessment of voice and speech disorders. Acoustical analysis of speech has long been considered a promising approach to non-invasive and objective assessment of people. In the past the types and amount of speech materials used for acoustical assessment were very limited. With the ASR technology, we are able to perform acoustical and linguistic analyses with a large amount of natural speech from impaired speakers. The present study is focused on Cantonese, which is a major Chinese dialect. Two representative disorders of speech production are investigated: dysphonia and aphasia. ASR experiments are carried out with continuous and spontaneous speech utterances from Cantonese-speaking patients. The results confirm the feasibility and potential of using natural speech for acoustical assessment of voice and speech disorders, and reveal the challenging issues in acoustic modeling and language modeling of pathological speech.
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Many AI models have been trained and tested using datasets specific to certain languages, dialects, age groups, and/or disorder subtypes, which limits the applicability of the AI models to larger broader populations.
“Many AI models have been trained and tested using datasets that are specific to certain languages, dialects, age groups, and/or disorder subtypes, which limits the applicability of the AI models to larger broader populations.16,41,42,55,56,76,95”
From The role of AI in the diagnosis of speech and language disorders: A systematic mapping study (2025) · cites this paper as [55]
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Cited by
Sorted by citations
Automatic quantitative analysis of spontaneous aphasic speech
Le, D., Licata, K., & Mower Provost, E. (2018) · Speech Communication · doi:10.1016/j.specom.2018.04.001 · Cited by 97
An End-to-End Approach to Automatic Speech Assessment for Cantonese-speaking People with Aphasia
Qin, Y., Wu, Y., Lee, T., & Kong, A. P. H. (2020) · Journal of Signal Processing Systems · doi:10.1007/s11265-019-01511-3 · Cited by 25
“A general-purpose ASR system was applied on PWA speech in [14] and a higher syllable error rate was observed for aphasia subjects compared with unimpaired subjects.”
MethodSelf-citationHerath, H. M. D. P. M., Weraniyagoda, W. A. S. A., Rajapaksha, R. T. M., Wijesekara, P. A. D. S. N., Sudheera, K. L. K., & Chong, P. H. J. (2022) · Sensors · doi:10.3390/s22186966 · Cited by 23
Toward Real-World Voice Disorder Classification
Kuo, H.-C., Hsieh, Y.-P., Tseng, H.-H., Wang, C.-T., Fang, S.-H., & Tsao, Y. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3270532 · Cited by 10
Towards automatic assessment of aphasia speech using automatic speech recognition techniques
Qin, Y., Lee, T., Kong, A. P. H., & Law, S. P. (2016) · 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2016.7918445 · Cited by 7
Self-citationThe role of AI in the diagnosis of speech and language disorders: A systematic mapping study
Tbaishat, D., Al-Shafei, R., & Odeh, M. (2025) · DIGITAL HEALTH · doi:10.1177/20552076251379769 · Cited by 4
Metadata
- Authors
- Lee, T., Liu, Y., Huang, P.-W., Chien, J.-T., Lam, W. K., Yeung, Y. T., Law, T. K. T., Lee, K. Y. S., Kong, A. P.-H., & Law, S.-P.
- Affiliations
- Chinese University of Hong Kong, Shenzhen (CN)
- National Yang Ming Chiao Tung University (TW)
- Chinese University of Hong Kong (HK)
- University of Central Florida (US)
- University of Hong Kong (HK)
- ORCID
- Venue
- 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 6475-6479
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2404317780
- Cited by
- 44
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 5 · 2023: 6 · 2022: 2 · 2021: 5 · 2020: 4 · 2019: 5 · 2018: 6 · 2017: 5 · 2016: 3
- FWCI
- 3.35
- Type
- conference paper
- Language
- en
- Open access
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speech recognition, Cantonese, pathological voice, dysphonia, aphasia, speech disorders, spontaneous speech, acoustic modeling, language modeling, objective assessment
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