| Presentation | 1996/12/13 Task adaptation of a stochastic language model for dialogue speech recognition Akinori Ito, Masaki Kohda, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A stochastic language model (SLM) is indispensable for continuous speech recognition. Generally,large corpus of the task domain is required to make a good SLM. When making a SLM for a specific task domain, it is ideal to obtain large number of sentences of the domain. But it takes large time and effort to collect linguistic data of a specific domain, especially of a spoken dialog domain. In this paper, we investigated possibility of making a good N-gram SLM using small corpus of a specific domain with task independent large corpus. Sightseeing information dialog task was chosen for the specific task, and we examined several kinds of corpora for task independent corpus. We carried out experiments to measure perplexity of the adapted N-gram model. From the experiments, it is found that the adaptation improved perplexity of the model when the task domain of the small and large corpora are similar. The results also showed that the coherence of morphemic analysis of the small and large corpora greatly affects the perplexity of the adapted model. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | continuous speech recognition / stochastic language model / N-gram / task adaptation |
| Paper # | NLC96-50,SP96-81 |
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| Conference Information | |
| Committee | NLC |
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| Conference Date | 1996/12/13(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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| Vice Chair | |
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| Paper Information | |
| 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) | Task adaptation of a stochastic language model for dialogue speech recognition |
| Sub Title (in English) | |
| Keyword(1) | continuous speech recognition |
| Keyword(2) | stochastic language model |
| Keyword(3) | N-gram |
| Keyword(4) | task adaptation |
| 1st Author's Name | Akinori Ito |
| 1st Author's Affiliation | Faculty of Engineering, Yamagata University() |
| 2nd Author's Name | Masaki Kohda |
| 2nd Author's Affiliation | Faculty of Engineering, Yamagata University |
| Date | 1996/12/13 |
| Paper # | NLC96-50,SP96-81 |
| Volume (vol) | vol.96 |
| Number (no) | 420 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |