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

Sheng LI; Yuya AKITA; Tatsuya KAWAHARA

doi:10.1587/transinf.2015EDP7047

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

Sheng LI, Yuya AKITA, Tatsuya KAWAHARA

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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.

Publication: IEICE TRANSACTIONS on Information 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

Authors

Sheng LI
  Kyoto University
Yuya AKITA
  Kyoto University
Tatsuya KAWAHARA
  Kyoto University

Keyword

speech recognition, acoustic model, lightly supervised training, lecture transcription

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Sheng LI, Yuya AKITA, Tatsuya KAWAHARA, "Automatic Lecture Transcription Based on Discriminative Data Selection for Lightly Supervised Acoustic Model Training" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 8, pp. 1545-1552, August 2015, doi: 10.1587/transinf.2015EDP7047.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7047/_p

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@ARTICLE{e98-d_8_1545,
author={Sheng LI, Yuya AKITA, Tatsuya KAWAHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Lecture Transcription Based on Discriminative Data Selection for Lightly Supervised Acoustic Model Training},
year={2015},
volume={E98-D},
number={8},
pages={1545-1552},
abstract={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.},
keywords={},
doi={10.1587/transinf.2015EDP7047},
ISSN={1745-1361},
month={August},}

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TY - JOUR
TI - Automatic Lecture Transcription Based on Discriminative Data Selection for Lightly Supervised Acoustic Model Training
T2 - IEICE TRANSACTIONS on Information
SP - 1545
EP - 1552
AU - Sheng LI
AU - Yuya AKITA
AU - Tatsuya KAWAHARA
PY - 2015
DO - 10.1587/transinf.2015EDP7047
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2015
AB - 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.
ER -