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Title: Semi-Supervised Acoustic Model Training by Discriminative Data Selection from Multiple ASR Systems' Hypotheses
Authors: Li, Sheng
Akita, Yuya
Kawahara, Tatsuya
Author's alias: 河原, 達也
Keywords: speech recognition
acoustic model
semi-supervised training
lecture transcription
Issue Date: 3-May-2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Journal title: IEEE/ACM Transactions on Audio Speech and Language Processing
Volume: 24
Issue: 9
Start page: 1524
End page: 1534
Abstract: While the performance of ASR systems depends on the size of the training data, it is very costly to prepare accurate and faithful transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In the proposed method, we obtain ASR hypotheses by complementary GMM-and DNN-based ASR systems. Then, a set of CRF-based classifiers is trained to select the correct hypotheses and verify the selected data. The proposed hypothesis combination shows higher quality compared with the conventional system combination method (ROVER). Moreover, compared with the conventional data selection based on confidence measure score, our method is demonstrated more effective for filtering usable data. Significant improvement in the ASR accuracy is achieved over the baseline system and in comparison with the models trained with the conventional system combination and data selection methods.
Rights: ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/219417
DOI(Published Version): 10.1109/TASLP.2016.2562505
Appears in Collections:Journal Articles

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