| Presentation | 2004/12/13 MINIMUM BAYES RISK ESTIMATION AND DECODING IN LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION William Byrne, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Minimum risk estimation and decoding strategies based on lattice segmentation techniques can be used to refine large vocabulary continuous speech recognition systems through the estimation of the parameters of the underlying hidden Mark models and through the identification of smaller recognition tasks which provides the opportunity to incorporate novel modeling and decoding procedures in LVCSR. These techniques are discussed in the context of going 'beyond HMMs'. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | |
| Paper # | NLC2004-46,SP2004-86 |
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| Conference Information | |
| Committee | NLC |
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| Conference Date | 2004/12/13(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Natural Language Understanding and Models of Communication (NLC) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | MINIMUM BAYES RISK ESTIMATION AND DECODING IN LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION |
| Sub Title (in English) | |
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| 1st Author's Name | William Byrne |
| 1st Author's Affiliation | Department of Engineering, Cambridge University() |
| Date | 2004/12/13 |
| Paper # | NLC2004-46,SP2004-86 |
| Volume (vol) | vol.104 |
| Number (no) | 538 |
| Page | pp.pp.- |
| #Pages | 6 |
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