NDLI: Detection of precisely transcribed parts from inexact transcribed corpus

Content Provider	IEEE Xplore Digital Library
Author	Ohta, K. Tsuchiya, M. Nakagawa, S.
Copyright Year	2011
Description	Author affiliation: Department of Information and Computer Sciences, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpakucho, Toyohashi-shi, Aichi, 441-8580 Japan (Ohta, K.; Nakagawa, S.) \|\| Information and Media Center, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpakucho, Toyohashi-shi, Aichi, 441-8580 Japan (Tsuchiya, M.)
Abstract	Although large-scale spontaneous speech corpora are crucial resource for various domains of spoken language processing, they are usually limited due to their construction cost especially in transcribing precisely. On the other hand, inexact transcribed corpora like shorthand notes, meeting records and closed captions are widely available. Unfortunately, it is difficult to use them directly as speech corpora for learning acoustic models, because they contain two kinds of text, precisely transcribed parts and edited parts. In order to resolve this problem, this paper proposes an automatic detection method of precisely transcribed parts from inexact transcribed corpora. Our method consists of two steps: the first step is an automatic alignment between the inexact transcription and its corresponding utterance, and the second step is a support vector machine based detector of precisely transcribed parts using several features obtained by the first step. Experiments using the Japanese National Diet Record shows that automatic detection of precise parts is effective for lightly supervised speaker adaptation, and shows that it achieves reasonable performance to reduce the converting cost from inexact transcribed corpora into precisely transcribed ones.
Starting Page	541
Ending Page	546
File Size	383000
Page Count	6
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163989
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Acoustics Speech Training Predictive models Adaptation models Support vector machines
Content Type	Text
Resource Type	Article

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