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Active Learning Using Phone-Error Distribution for Speech Modeling
Hiroko MURAKAMI Koichi SHINODA Sadaoki FURUI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2012/10/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech and Hearing
active learning, speaker adaptation, acoustic modeling, phone error distribution, Kullback-Leibler divergence,
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We propose an active learning framework for speech recognition that reduces the amount of data required for acoustic modeling. This framework consists of two steps. We first obtain a phone-error distribution using an acoustic model estimated from transcribed speech data. Then, from a text corpus we select a sentence whose phone-occurrence distribution is close to the phone-error distribution and collect its speech data. We repeat this process to increase the amount of transcribed speech data. We applied this framework to speaker adaptation and acoustic model training. Our evaluation results showed that it significantly reduced the amount of transcribed data while maintaining the same level of accuracy.