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arXiv · 1904.10045

Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition

Shiliang Zhang·Ming Lei·Zhijie Yan
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Abstract

Connectionist Temporal Classification (CTC) based end-to-end speech recognition system usually need to incorporate an external language model by using WFST-based decoding in order to achieve promising results. This is more essential to Mandarin speech recognition since it owns a special phenomenon, namely homophone, which causes a lot of substitution errors. The linguistic information introduced by language model will help to distinguish these substitution errors. In this work, we propose a transformer based spelling correction model to automatically correct errors especially the substitution errors made by CTC-based Mandarin speech recognition system. Specifically, we investigate using the recognition results generated by CTC-based systems as input and the ground-truth transcriptions as output to train a transformer with encoder-decoder architecture, which is much similar to machine translation. Results in a 20,000 hours Mandarin speech recognition task show that the proposed spelling correction model can achieve a CER of 3.41%, which results in 22.9% and 53.2% relative improvement compared to the baseline CTC-based systems decoded with and without language model respectively.

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BibTeXRIS

Shiliang Zhang, Ming Lei, Zhijie Yan. 2019-03-27. Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition. https://arxiv.org/abs/1904.10045

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