- 著者
- Sheng LI Yuya AKITA Tatsuya KAWAHARA
- 出版者
- 一般社団法人 電子情報通信学会
- 雑誌
- IEICE Transactions on Information and Systems (ISSN:09168532)
- 巻号頁・発行日
- vol.E98.D, no.8, pp.1545-1552, 2015-08-01 (Released:2015-08-01)
- 参考文献数
- 29
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.