Contextual-Utterance Training for Automatic Speech Recognition

• URL: http://arxiv.org/abs/2210.16238v1
• Date: Thu, 27 Oct 2022 08:10:44 GMT
• Title: Contextual-Utterance Training for Automatic Speech Recognition
• Authors: Alejandro Gomez-Alanis, Lukas Drude, Andreas Schwarz, Rupak Vignesh Swaminathan, Simon Wiesler
• Abstract summary: We propose a contextual-utterance training technique which makes use of the previous and future contextual utterances. Also, we propose a dual-mode contextual-utterance training technique for streaming automatic speech recognition (ASR) systems. The proposed technique is able to reduce both the WER and the average last token emission latency by more than 6% and 40ms relative.
• Score: 65.4571135368178
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recent studies of streaming automatic speech recognition (ASR) recurrent neural network transducer (RNN-T)-based systems have fed the encoder with past contextual information in order to improve its word error rate (WER) performance. In this paper, we first propose a contextual-utterance training technique which makes use of the previous and future contextual utterances in order to do an implicit adaptation to the speaker, topic and acoustic environment. Also, we propose a dual-mode contextual-utterance training technique for streaming automatic speech recognition (ASR) systems. This proposed approach allows to make a better use of the available acoustic context in streaming models by distilling "in-place" the knowledge of a teacher, which is able to see both past and future contextual utterances, to the student which can only see the current and past contextual utterances. The experimental results show that a conformer-transducer system trained with the proposed techniques outperforms the same system trained with the classical RNN-T loss. Specifically, the proposed technique is able to reduce both the WER and the average last token emission latency by more than 6% and 40ms relative, respectively.

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