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Word-level beam search decoding and correction algorithm (WLBS) for end-to-end ASR

Sasindran, Z and Tamma, PV (2021) Word-level beam search decoding and correction algorithm (WLBS) for end-to-end ASR. In: 1st International Conference on AI-ML-Systems, AIMLSystems 2021, 21-23 Oct 2021.

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Official URL: https://doi.org/10.1145/3486001.3486223


A key challenge in resource-constrained speech recognition applications is the unavailability of a large, domain-specific audio corpus to train the models. In such scenarios, models may not be exposed to a wide range of domain-specific words and phrases. In this work, we propose an approach to improve the in-domain automatic speech recognition results using our word-level beam search decoding and correction algorithm (WLBS). We use a token-based language model to mitigate the data sparsity and the out of vocabulary issues in the corpus. We evaluate the proposed approach for airplane-cabin specific announcements use case. The experimental results show that the WLBS algorithm with its handling of misspellings and missing words achieves better performance than state-of-the-art beam search decoding and n-gram LMs. We report a WER of 11.48 on our airplane-cabin announcement test corpus. © 2021 ACM.

Item Type: Conference Paper
Publication: ACM International Conference Proceeding Series
Publisher: Association for Computing Machinery
Additional Information: The copyright for this article belongs to Association for Computing Machinery
Keywords: Aircraft; Computational linguistics; Speech recognition, Automatic speech recognition; Beam search; Beam search decoding; Correction algorithms; Decoding algorithm; Language model; Sequence models; Sequence-to-sequence model; Token-based language model; Word level, Decoding
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 25 Nov 2021 10:32
Last Modified: 25 Nov 2021 10:32
URI: http://eprints.iisc.ac.in/id/eprint/70493

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