Improving code-switched ASR with linguistic information

Jie Chi, Peter Bell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Computational Linguistics
EditorsNicoletta 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
PublisherACL Anthology
Number of pages6
Publication statusPublished - 3 Nov 2022
EventThe 29th International Conference on Computational Linguistics, 2022 - Gyeongju, Korea, Republic of
Duration: 12 Oct 202217 Oct 2022
Conference number: 29

Publication series

PublisherACL Anthology
ISSN (Electronic)2591-2093


ConferenceThe 29th International Conference on Computational Linguistics, 2022
Abbreviated titleCOLING 2022
Country/TerritoryKorea, Republic of
Internet address


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