Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction

• URL: http://arxiv.org/abs/2104.12870v1
• Date: Mon, 26 Apr 2021 20:38:42 GMT
• Title: Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
• Authors: David Qiu, Yanzhang He, Qiujia Li, Yu Zhang, Liangliang Cao, Ian McGraw
• Abstract summary: Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems. Recent works have proposed using neural networks to learn word or utterance confidence scores for end-to-end ASR. This paper proposes to jointly learn word confidence, word deletion, and utterance confidence.
• Score: 20.00640459241358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems. Recent works have proposed using neural networks to learn word or utterance confidence scores for end-to-end ASR. In those studies, word confidence by itself does not model deletions, and utterance confidence does not take advantage of word-level training signals. This paper proposes to jointly learn word confidence, word deletion, and utterance confidence. Empirical results show that multi-task learning with all three objectives improves confidence metrics (NCE, AUC, RMSE) without the need for increasing the model size of the confidence estimation module. Using the utterance-level confidence for rescoring also decreases the word error rates on Google's Voice Search and Long-tail Maps datasets by 3-5% relative, without needing a dedicated neural rescorer.

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