Improving Code-switched ASR with Linguistic Information

Jie Chi, Peter Bell

Abstract
This paper seeks to improve the performance of automatic speech recognition (ASR) systems operating on code-switched speech. Code-switching refers to the alternation of languages within a conversation, a phenomenon that is of increasing importance considering the rapid rise in the number of bilingual speakers in the world. It is particularly challenging for ASR owing to the relative scarcity of code-switching speech and text data, even when the individual languages are themselves well-resourced. This paper proposes to overcome this challenge by applying linguistic theories in order to generate more realistic code-switching text, necessary for language modelling in ASR. Working with English-Spanish code-switching, we find that Equivalence Constraint theory and part-of-speech labelling are particularly helpful for text generation, and bring 2% improvement to ASR performance.
Anthology ID:
2022.coling-1.627
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:
7171–7176
Language:
URL:
https://aclanthology.org/2022.coling-1.627
DOI:
Bibkey:
Cite (ACL):
Jie Chi and Peter Bell. 2022. Improving Code-switched ASR with Linguistic Information. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7171–7176, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Improving Code-switched ASR with Linguistic Information (Chi & Bell, COLING 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.coling-1.627.pdf
PDF Search