Language-specific Effects on Automatic Speech Recognition Errors for World Englishes

June Choe, Yiran Chen, May Pik Yu Chan, Aini Li, Xin Gao, Nicole Holliday


Abstract
Despite recent advancements in automated speech recognition (ASR) technologies, reports of unequal performance across speakers of different demographic groups abound. At the same time, the focus on performance metrics such as the Word Error Rate (WER) in prior studies limit the specificity and scope of recommendations that can be offered for system engineering to overcome these challenges. The current study bridges this gap by investigating the performance of Otter’s automatic captioning system on native and non-native English speakers of different language background through a linguistic analysis of segment-level errors. By examining language-specific error profiles for vowels and consonants motivated by linguistic theory, we find that certain categories of errors can be predicted from the phonological structure of a speaker’s native language.
Anthology ID:
2022.coling-1.628
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
7177–7186
Language:
URL:
https://aclanthology.org/2022.coling-1.628
DOI:
Bibkey:
Cite (ACL):
June Choe, Yiran Chen, May Pik Yu Chan, Aini Li, Xin Gao, and Nicole Holliday. 2022. Language-specific Effects on Automatic Speech Recognition Errors for World Englishes. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7177–7186, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes (Choe et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.628.pdf