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arXiv · 2004.03437

Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition

Yi Zheng·Xianjie Yang·Xuyong Dang
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Abstract

A new label smoothing method that makes use of prior knowledge of a language at human level, homophone, is proposed in this paper for automatic speech recognition (ASR). Compared with its forerunners, the proposed method uses pronunciation knowledge of homophones in a more complex way. End-to-end ASR models that learn acoustic model and language model jointly and modelling units of characters are necessary conditions for this method. Experiments with hybrid CTC sequence-to-sequence model show that the new method can reduce character error rate (CER) by 0.4% absolutely.

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BibTeXRIS

Yi Zheng, Xianjie Yang, Xuyong Dang. 2020-04-07. Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition. https://arxiv.org/abs/2004.03437

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