Presentation | 2006/12/15 Cross-validation EM Algorithm for Robust Parameter Estimation Takahiro SHINOZAKI, Mari OSTENDORF, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A new maximum likelihood training algorithm is proposed that compensates for weaknesses of the EM algorithm by using cross-validation likelihood in the expectation step to avoid overtraining. By using a set of sufficient statistics associated with a partitioning of the training data, as in parallel EM, the algorithm has the same order of computational requirements as the original EM algorithm. Analyses using a GMM with artificial data show the proposed algorithm is more robust for overtraining than the conventional EM algorithm. Large vocabulary recognition experiments on Mandarin broadcast news data show that the method makes better use of more parameters and gives lower recognition error rates than EM training. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | EM training / overtraining / cross-validation |
Paper # | NLC2006-61,SP2006-117 |
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Committee | NLC |
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Conference Date | 2006/12/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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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) | Cross-validation EM Algorithm for Robust Parameter Estimation |
Sub Title (in English) | |
Keyword(1) | EM training |
Keyword(2) | overtraining |
Keyword(3) | cross-validation |
1st Author's Name | Takahiro SHINOZAKI |
1st Author's Affiliation | Academic Center for Computing and Media Studies [South Building], Kyoto University() |
2nd Author's Name | Mari OSTENDORF |
2nd Author's Affiliation | Department of Electrical Engineering, University of Washington |
Date | 2006/12/15 |
Paper # | NLC2006-61,SP2006-117 |
Volume (vol) | vol.106 |
Number (no) | 442 |
Page | pp.pp.- |
#Pages | 6 |
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