Data centric approach to Chinese Medical Speech Recognition

Sheng-Luen Chung, Yi-Shiuan Li, Hsien-Wei Ting

Abstract
Concerning the development of Chinese medical speech recognition technology, this study re-addresses earlier encountered issues in accordance with the process of Machine Learning Engineering for Production (MLOps) from a data centric perspective. First is the new segmentation of speech utterances to meet sentences completeness for all utterances in the collected Chinese Medical Speech Corpus (ChiMeS). Second is optimization of Joint CTC/Attention model through data augmentation in boosting recognition performance out of very limited speech corpus. Overall, to facilitate the development of Chinese medical speech recognition, this paper contributes: (1) The ChiMeS corpus, the first Chinese Medicine Speech corpus of its kind, which is 14.4 hours, with a total of 7,225 sentences. (2) A trained Joint CTC/Attention ASR model by ChiMeS-14, yielding a Character Error Rate (CER) of 13.65% and a Keyword Error Rate (KER) of 20.82%, respectively, when tested on the ChiMeS-14 testing set. And (3) an evaluation platform set up to compare performance of other ASR models. All the released resources can be found in the ChiMeS portal (https://iclab.ee.ntust.edu.tw/home).
Anthology ID:
2021.rocling-1.10
Volume:
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
Month:
October
Year:
2021
Address:
Taoyuan, Taiwan
Editors:
Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Venue:
ROCLING
SIG:
Publisher:
The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Note:
Pages:
72–80
Language:
URL:
https://aclanthology.org/2021.rocling-1.10
DOI:
Bibkey:
Cite (ACL):
Sheng-Luen Chung, Yi-Shiuan Li, and Hsien-Wei Ting. 2021. Data centric approach to Chinese Medical Speech Recognition. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 72–80, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
Cite (Informal):
Data centric approach to Chinese Medical Speech Recognition (Chung et al., ROCLING 2021)
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