Contextual information plays a crucial role in speech recognition technologies and incorporating it into the end-to-end speech recognition models has drawn immense interest recently. However, previous deep bias methods lacked explicit supervision for bias tasks. In this study, we introduce a contextual phrase prediction network for an attention-based deep bias method. This network predicts context phrases in utterances using contextual embeddings and calculates bias loss to assist in the training of the contextualized model. Our method achieved a significant word error rate (WER) reduction across various end-to-end speech recognition models. Experiments on the LibriSpeech corpus show that our proposed model obtains a 12.1% relative WER improvement over the baseline model, and the WER of the context phrases decreases relatively by 40.5%. Moreover, by applying a context phrase filtering strategy, we also effectively eliminate the WER degradation when using a larger biasing list.
@article{arxiv.2305.12493,
title = {Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network},
author = {Kaixun Huang and Ao Zhang and Zhanheng Yang and Pengcheng Guo and Bingshen Mu and Tianyi Xu and Lei Xie},
journal= {arXiv preprint arXiv:2305.12493},
year = {2023}
}