An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement

• URL: http://arxiv.org/abs/2402.17189v1
• Date: Tue, 27 Feb 2024 04:08:59 GMT
• Title: An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement
• Authors: Tzu-Ting Yang, Hsin-Wei Wang, Yi-Cheng Wang, Chi-Han Lin, and Berlin Chen
• Abstract summary: Codeswitching phenomenon remains a major obstacle that hinders automatic speech recognition. We introduce a novel disentanglement loss to enable the lower-layer of the encoder to capture inter-lingual acoustic information. We verify that our proposed method outperforms the prior-art methods using pretrained dual-encoders.
• Score: 9.28943772676672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR). However, the codeswitching phenomenon remains a major obstacle that hinders ASR from perfection, as the lack of labeled data and the variations between languages often lead to degradation of ASR performance. In this paper, we focus exclusively on improving the acoustic encoder of E2E ASR to tackle the challenge caused by the codeswitching phenomenon. Our main contributions are threefold: First, we introduce a novel disentanglement loss to enable the lower-layer of the encoder to capture inter-lingual acoustic information while mitigating linguistic confusion at the higher-layer of the encoder. Second, through comprehensive experiments, we verify that our proposed method outperforms the prior-art methods using pretrained dual-encoders, meanwhile having access only to the codeswitching corpus and consuming half of the parameterization. Third, the apparent differentiation of the encoders' output features also corroborates the complementarity between the disentanglement loss and the mixture-of-experts (MoE) architecture.

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