Related papers: Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search

Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search

URL: http://arxiv.org/abs/2401.10449v1
Date: Fri, 19 Jan 2024 01:36:07 GMT
Title: Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search
Authors: Yui Sudo, Muhammad Shakeel, Yosuke Fukumoto, Yifan Peng, Shinji Watanabe
Abstract summary: This paper proposes an attention-based contextual biasing method that can be customized using an editable phrase list. The proposed method can be trained effectively by combining a bias phrase index loss and special tokens to detect the bias phrases in the input speech data.
Score: 44.94458898538114
License: http://creativecommons.org/licenses/by/4.0/
Abstract: End-to-end (E2E) automatic speech recognition (ASR) methods exhibit remarkable performance. However, since the performance of such methods is intrinsically linked to the context present in the training data, E2E-ASR methods do not perform as desired for unseen user contexts (e.g., technical terms, personal names, and playlists). Thus, E2E-ASR methods must be easily contextualized by the user or developer. This paper proposes an attention-based contextual biasing method that can be customized using an editable phrase list (referred to as a bias list). The proposed method can be trained effectively by combining a bias phrase index loss and special tokens to detect the bias phrases in the input speech data. In addition, to improve the contextualization performance during inference further, we propose a bias phrase boosted (BPB) beam search algorithm based on the bias phrase index probability. Experimental results demonstrate that the proposed method consistently improves the word error rate and the character error rate of the target phrases in the bias list on both the Librispeech-960 (English) and our in-house (Japanese) dataset, respectively.

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