Presentation | 2009/12/14 Active learning using multiple recognizers for speech recognition Yuzo Hamanaka, Tadashi Emori, Takafumi Koshinaka, Koichi Shinoda, Sadaoki Furui, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We propose an active learning method with multiple recognizers for large vocabulary continuous speech recognition. In this approach, the recognition results obtained from recognizers are used for selecting utterances. Here, a progressive search method is used for aligning sentences, and voting entropy is used as a measure for selecting utterances. Our method was evaluated by using 190-hour speech data in the Corpus of Spontaneous Japanese. It proved to be significantly better than random selection. It only required 60 h of data to achieve a word accuracy of 74%, while standard training(i.e., random selection)required 97 h of data. The recognition accuracy of our proposed method was also better than that of the conventional uncertainty sampling method using word posterior probabilities as the confidence measures for selecting sentences. |
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Committee | NLC |
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Conference Date | 2009/12/14(1days) |
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Registration To | Natural Language Understanding and Models of Communication (NLC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Active learning using multiple recognizers for speech recognition |
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1st Author's Name | Yuzo Hamanaka |
1st Author's Affiliation | Tokyo Institute of Technology() |
2nd Author's Name | Tadashi Emori |
2nd Author's Affiliation | NEC Informatec Systems, Ltd. |
3rd Author's Name | Takafumi Koshinaka |
3rd Author's Affiliation | Tokyo Institute of Technology:NEC Corporation |
4th Author's Name | Koichi Shinoda |
4th Author's Affiliation | Tokyo Institute of Technology |
5th Author's Name | Sadaoki Furui |
5th Author's Affiliation | Tokyo Institute of Technology |
Date | 2009/12/14 |
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Volume (vol) | vol.109 |
Number (no) | 355 |
Page | pp.pp.- |
#Pages | 5 |
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