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

• URL: http://arxiv.org/abs/2004.03437v2
• Date: Thu, 14 May 2020 07:13:13 GMT
• Title: Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition
• Authors: Yi Zheng, Xianjie Yang, Xuyong Dang
• Abstract summary: The proposed method uses pronunciation knowledge of homophones in a more complex way. Experiments with hybrid CTC sequence-to-sequence model show that the new method can reduce character error rate (CER) by 0.4% absolutely.
• Score: 8.066444614339614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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