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Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition ...
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Abstract:
Modeling code-switched speech is an important problem in automatic speech recognition (ASR). Labeled code-switched data are rare, so monolingual data are often used to model code-switched speech. These monolingual data may be more closely matched to one of the languages in the code-switch pair. We show that such asymmetry can bias prediction toward the better-matched language and degrade overall model performance. To address this issue, we propose a semi-supervised approach for code-switched ASR. We consider the case of English-Mandarin code-switching, and the problem of using monolingual data to build bilingual "transcription models'' for annotation of unlabeled code-switched data. We first build multiple transcription models so that their individual predictions are variously biased toward either English or Mandarin. We then combine these biased transcriptions using confidence-based selection. This strategy generates a superior transcript for semi-supervised training, and obtains a 19% relative improvement ... : 5 pages ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2106.07699
https://arxiv.org/abs/2106.07699
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Advances in Transcription of Broadcast News and Conversational Telephone Speech Within the Combined EARS BBN/LIMSI System
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In: ISSN: 1558-7916 ; IEEE Transactions on Audio, Speech and Language Processing ; https://hal.archives-ouvertes.fr/hal-01299058 ; IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2006 (2006)
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