NDLI: Designing text corpus using phone-error distribution for acoustic modeling

Content Provider	IEEE Xplore Digital Library
Author	Murakami, H. Shinoda, K. Furui, S.
Copyright Year	2011
Description	Author affiliation: Dept. Computer Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, 152-8552, Japan (Murakami, H.; Shinoda, K.; Furui, S.)
Abstract	It is expensive to prepare a sufficient amount of training data for acoustic modeling for developing large vocabulary continuous speech recognition systems. This is a serious problem especially for resource-deficient languages. We propose an active learning method that effectively reduces the amount of training data without any degradation in recognition performance. It is used to design a text corpus for read speech collection. It first estimates phone-error distribution using a small amount of fully transcribed speech data. Second, it constructs a sentence set whose phone-occurrence distribution is close to the phone-error distribution and collects its speech data. It then extends this process to diphones and triphones and collects more speech data. We evaluated our method with simulation experiments using the Corpus of Spontaneous Japanese. It required only 76 h of speech data to achieve word accuracy of 74.7%, while the conventional training method required 152 h of data to achieve the same rate.
Starting Page	191
Ending Page	195
File Size	491674
Page Count	5
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163929
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	Speech Training data Training Accuracy Acoustics Data models Hidden Markov models
Content Type	Text
Resource Type	Article

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