Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization

• URL: http://arxiv.org/abs/2111.15016v1
• Date: Mon, 29 Nov 2021 23:14:54 GMT
• Title: Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
• Authors: Brian Yan, Chunlei Zhang, Meng Yu, Shi-Xiong Zhang, Siddharth Dalmia, Dan Berrebbi, Chao Weng, Shinji Watanabe, Dong Yu
• Abstract summary: We propose a general framework to jointly model the likelihoods of the monolingual and code-switch sub-tasks that comprise bilingual speech recognition. We demonstrate the efficacy of our proposed model on bilingual Mandarin-English speech recognition across both monolingual and code-switched corpora.
• Score: 75.98664099579392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a general framework to jointly model the likelihoods of the monolingual and code-switch sub-tasks that comprise bilingual speech recognition. By defining the monolingual sub-tasks with label-to-frame synchronization, our joint modeling framework can be conditionally factorized such that the final bilingual output, which may or may not be code-switched, is obtained given only monolingual information. We show that this conditionally factorized joint framework can be modeled by an end-to-end differentiable neural network. We demonstrate the efficacy of our proposed model on bilingual Mandarin-English speech recognition across both monolingual and code-switched corpora.

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