DialectMoE: An End-to-End Multi-Dialect Speech Recognition Model with Mixture-of-Experts

Anonymous

Abstract: Dialect speech recognition has always been one of the challenges in Automatic Speech Recognition (ASR) systems. While lots of ASR systems perform well in Mandarin, their performance significantly drops when handling dialect speech. This is mainly due to the obvious differences between dialects and Mandarin in pronunciation and the data scarcity of dialect speech. In this paper, we propose DialectMoE, a Chinese multi-dialects speech recognition model based on Mixture-of-Experts (MoE) in a low-resource conditions. Specifically, DialectMoE assigns input sequences to a set of experts using a dynamic routing algorithm, with each expert potentially trained for a specific dialect. Subsequently, the outputs of these experts are combined to derive the final output. Due to the similarities among dialects, distinct experts may offer assistance in recognizing other dialects as well. Experimental results on the AiDatatang dialect public dataset show that, compared with the baseline model, DialectMoE reduces Character Error Rate(CER) for Sichuan, Yunnan, Hubei and Henan dialects by 23.6\%, 32.6\%, 39.2\% and 35.09\% respectively. The proposed DialectMoE model demonstrates outstanding performance in multi-dialects speech recognition.

Paper Type: long

Research Area: Speech recognition, text-to-speech and spoken language understanding

Contribution Types: Model analysis & interpretability, Approaches to low-resource settings

Languages Studied: Chinese Mandarin, Chinese Yunnan dialect, Chinese Sichuan dialect.