Enhancing Code-Switching Speech Recognition with LID-Based Collaborative Mixture of Experts Model

• URL: http://arxiv.org/abs/2409.02050v2
• Date: Thu, 5 Sep 2024 11:54:52 GMT
• Title: Enhancing Code-Switching Speech Recognition with LID-Based Collaborative Mixture of Experts Model
• Authors: Hukai Huang, Jiayan Lin, Kaidi Wang, Yishuang Li, Wenhao Guan, Lin Li, Qingyang Hong,
• Abstract summary: This study proposes a Collaborative-MoE, a Mixture of Experts (MoE) model that leverages a collaborative mechanism among expert groups. Within each language expert group, a gating network operates unsupervised to foster collaboration on attributes beyond language. Our method preserves the efficient inference capabilities characteristic of MoE models without necessitating additional pre-training.
• Score: 12.030995417911296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the inherent difficulty in modeling phonetic similarities across different languages, code-switching speech recognition presents a formidable challenge. This study proposes a Collaborative-MoE, a Mixture of Experts (MoE) model that leverages a collaborative mechanism among expert groups. Initially, a preceding routing network explicitly learns Language Identification (LID) tasks and selects experts based on acquired LID weights. This process ensures robust routing information to the MoE layer, mitigating interference from diverse language domains on expert network parameter updates. The LID weights are also employed to facilitate inter-group collaboration, enabling the integration of language-specific representations. Furthermore, within each language expert group, a gating network operates unsupervised to foster collaboration on attributes beyond language. Extensive experiments demonstrate the efficacy of our approach, achieving significant performance enhancements compared to alternative methods. Importantly, our method preserves the efficient inference capabilities characteristic of MoE models without necessitating additional pre-training.

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