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A grapheme-based method for automatic alignment of speech and text data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationSpoken Language Technology Workshop (SLT), 2012 IEEE
PublisherIEEE
Pages286-290
Number of pages5
ISBN (Electronic)978-1-4673-5125-6
ISBN (Print)978-1-4673-5124-9
DOIs
StatePublished - 2012

Abstract

This paper introduces a method for automatic alignment of speech data with unsynchronised, imperfect transcripts, for a domain where no initial acoustic models are available. Using grapheme-based acoustic models, word skip networks and orthographic speech transcripts, we are able to harvest 55% of the speech with a 93% utterance-level accuracy and 99% word accuracy for the produced transcriptions. The work is based on the assumption that there is a high degree of correspondence between the speech and text, and that a full transcription of all of the speech is not required. The method is language independent and the only prior knowledge and resources required are the speech and text transcripts, and a few minor user interventions.

    Research areas

  • acoustic signal processing, natural language processing, speech synthesis, text analysis, word processing, automatic speech data alignment, automatic text data alignment, grapheme-based acoustic models, language independent method, orthographic speech transcripts, text transcription, unsynchronised-imperfect transcripts, utterance-level accuracy, word accuracy, word skip networks, Acoustics, Data models, Error analysis, Hidden Markov models, Speech, Speech recognition, Training, grapheme-based models, imperfect transcripts, speech alignment, word networks

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