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arXiv · 2406.18862

Streaming Decoder-Only Automatic Speech Recognition with Discrete Speech Units: A Pilot Study

Peikun Chen·Sining Sun·Changhao Shan·Qing Yang·Lei Xie
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Abstract

Unified speech-text models like SpeechGPT, VioLA, and AudioPaLM have shown impressive performance across various speech-related tasks, especially in Automatic Speech Recognition (ASR). These models typically adopt a unified method to model discrete speech and text tokens, followed by training a decoder-only transformer. However, they are all designed for non-streaming ASR tasks, where the entire speech utterance is needed during decoding. Hence, we introduce a decoder-only model exclusively designed for streaming recognition, incorporating a dedicated boundary token to facilitate streaming recognition and employing causal attention masking during the training phase. Furthermore, we introduce right-chunk attention and various data augmentation techniques to improve the model's contextual modeling abilities. While achieving streaming speech recognition, experiments on the AISHELL-1 and -2 datasets demonstrate the competitive performance of our streaming approach with non-streaming decoder-only counterparts.

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Peikun Chen, Sining Sun, Changhao Shan, Qing Yang, Lei Xie. 2024-06-27. Streaming Decoder-Only Automatic Speech Recognition with Discrete Speech Units: A Pilot Study. https://arxiv.org/abs/2406.18862

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