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cs.CL2024
Enhancing Code-Switching Speech Recognition with LID-Based Collaborative Mixture of Experts Model
Hukai Huang, Jiayan Lin, Kaidi Wang +4
Due to the inherent difficulty in modeling phonetic similarities across different languages, code-switching speech recognition presents a formidable challenge. This study proposes…
cs.CL2024
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical Routing
Hukai Huang, Shenghui Lu, Yahui Shan +5
The Mixture of Experts (MoE) model is a promising approach for handling code-switching speech recognition (CS-ASR) tasks. However, the existing CS-ASR work on MoE has yet to levera…
eess.AS2024
LAFMA: A Latent Flow Matching Model for Text-to-Audio Generation
Wenhao Guan, Kaidi Wang, Wangjin Zhou +6
Recently, the application of diffusion models has facilitated the significant development of speech and audio generation. Nevertheless, the quality of samples generated by diffusio…