2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-22.1
Paper Title Code-Switch Speech Rescoring With Monolingual Data
Authors Guoyu Liu, Lixin Cao, Tencent, China
SessionSPE-22: Speech Recognition 8: Multilingual Speech Recognition
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Human Language Technology: [HLT-LANG] Language Modeling
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In the automatic speech recognition (ASR) system, how to solve the problem of code-switch speech recognition has been a concern. Code-switch speech recognition is challenging due to data scarcity as well as diverse syntactic structures across languages. In this paper, we focus on the code-switch speech recognition in mainland China, which is obviously different from the Hong Kong and Southeast Asia area in linguistic characteristics. We propose a novel approach that only uses monolingual data for code-switch second-pass speech recognition which is also named language model rescoring. The approach converts the code-switch sentence to a monolingual sentence by a word mapping and language model determination step, therefore the issue of data scarcity is unnecessary to be considered. The word pairs during the word mapping step are generated by a fine-designed generation process that incorporates machine translation, word alignment, etc. We show that the proposed approach achieves an over 7.23% relative WER reduction from the monolingual language model (MLM) rescoring in our test set.