Segmenting Subtitles for Correcting ASR Segmentation Errors

• URL: http://arxiv.org/abs/2104.07868v1
• Date: Fri, 16 Apr 2021 03:04:10 GMT
• Title: Segmenting Subtitles for Correcting ASR Segmentation Errors
• Authors: David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galu\v{s}\v{c}\'akov\'a, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
• Abstract summary: We propose a model for correcting the acoustic segmentation of ASR models for low-resource languages. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance.
• Score: 11.854481771567503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation. In this work, we propose a model for correcting the acoustic segmentation of ASR models for low-resource languages to improve performance on downstream tasks. We propose the use of subtitles as a proxy dataset for correcting ASR acoustic segmentation, creating synthetic acoustic utterances by modeling common error modes. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance on MT and audio-document cross-language information retrieval (CLIR).

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