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
Correct Metadata for
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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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
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@inproceedings{choe-etal-2022-language,
title = "Language-specific Effects on Automatic Speech Recognition Errors for World Englishes",
author = "Choe, June and
Chen, Yiran and
Chan, May Pik Yu and
Li, Aini and
Gao, Xin and
Holliday, Nicole",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.628/",
pages = "7177--7186",
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."
}
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%0 Conference Proceedings %T Language-specific Effects on Automatic Speech Recognition Errors for World Englishes %A Choe, June %A Chen, Yiran %A Chan, May Pik Yu %A Li, Aini %A Gao, Xin %A Holliday, Nicole %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F choe-etal-2022-language %X 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. %U https://aclanthology.org/2022.coling-1.628/ %P 7177-7186
Markdown (Informal)
[Language-specific Effects on Automatic Speech Recognition Errors for World Englishes](https://aclanthology.org/2022.coling-1.628/) (Choe et al., COLING 2022)
- Language-specific Effects on Automatic Speech Recognition Errors for World Englishes (Choe et al., COLING 2022)
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.