Automatic Lecture Transcription Based on Discriminative Data Selection for Lightly Supervised Acoustic Model Training

Sheng LI  Yuya AKITA  Tatsuya KAWAHARA  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.8   pp.1545-1552
Publication Date: 2015/08/01
Publicized: 2015/04/28
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7047
Type of Manuscript: PAPER
Category: Speech and Hearing
speech recognition,  acoustic model,  lightly supervised training,  lecture transcription,  

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The paper addresses a scheme of lightly supervised training of an acoustic model, which exploits a large amount of data with closed caption texts but not faithful transcripts. In the proposed scheme, a sequence of the closed caption text and that of the ASR hypothesis by the baseline system are aligned. Then, a set of dedicated classifiers is designed and trained to select the correct one among them or reject both. It is demonstrated that the classifiers can effectively filter the usable data for acoustic model training. The scheme realizes automatic training of the acoustic model with an increased amount of data. A significant improvement in the ASR accuracy is achieved from the baseline system and also in comparison with the conventional method of lightly supervised training based on simple matching.