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Title: Statistical Transformation of Language and Pronunciation Models for Spontaneous Speech Recognition
Authors: Akita, Yuya  kyouindb  KAKEN_id
Kawahara, Tatsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 秋田, 祐哉
Issue Date: Aug-2010
Publisher: IEEE
Journal title: IEEE Transactions on Audio, Speech, and Language Processing
Volume: 18
Issue: 6
Start page: 1539
End page: 1549
Abstract: We propose a novel approach based on a statistical transformation framework for language and pronunciation modeling of spontaneous speech. Since it is not practical to train a spoken-style model using numerous spoken transcripts, the proposed approach generates a spoken-style model by transforming an orthographic model trained with document archives such as the minutes of meetings and the proceedings of lectures. The transformation is based on a statistical model estimated using a small amount of a parallel corpus, which consists of faithful transcripts aligned with their orthographic documents. Patterns of transformation, such as substitution, deletion, and insertion of words, are extracted with their word and part-of-speech (POS) contexts, and transformation probabilities are estimated based on occurrence statistics in a parallel aligned corpus. For pronunciation modeling, subword-based mapping between baseforms and surface forms is extracted with their occurrence counts, then a set of rewrite rules with their probabilities are derived as a transformation model. Spoken-style language and pronunciation (surface forms) models can be predicted by applying these transformation patterns to a document-style language model and baseforms in a lexicon, respectively. The transformed models significantly reduced perplexity and word error rates (WERs) in a task of transcribing congressional meetings, even though the domains and topics were different from the parallel corpus. This result demonstrates the generality and portability of the proposed framework.
Rights: (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
DOI(Published Version): 10.1109/TASL.2009.2037400
Appears in Collections:Journal Articles

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