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arXiv · 2207.04176

Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition

Yizhou Peng·Yufei Liu·Jicheng Zhang·Haihua Xu·Yi He·Hao Huang·Eng Siong Chng
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Abstract

Internal Language Model Estimation (ILME) based language model (LM) fusion has been shown significantly improved recognition results over conventional shallow fusion in both intra-domain and cross-domain speech recognition tasks. In this paper, we attempt to apply our ILME method to cross-domain code-switching speech recognition (CSSR) work. Specifically, our curiosity comes from several aspects. First, we are curious about how effective the ILME-based LM fusion is for both intra-domain and cross-domain CSSR tasks. We verify this with or without merging two code-switching domains. More importantly, we train an end-to-end (E2E) speech recognition model by means of merging two monolingual data sets and observe the efficacy of the proposed ILME-based LM fusion for CSSR. Experimental results on SEAME that is from Southeast Asian and another Chinese Mainland CS data set demonstrate the effectiveness of the proposed ILME-based LM fusion method.

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Yizhou Peng, Yufei Liu, Jicheng Zhang, Haihua Xu, Yi He, Hao Huang, Eng Siong Chng. 2022-07-09. Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition. https://arxiv.org/abs/2207.04176

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