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- W2400358286 abstract "For automatic speech recognition, the construction of an adequate language model may be difficult when only a limited amount of training text is available. Previous work has shown that in the case of small training sets statistical language models may outperform grammars on out-of-coverage utterances, while showing comparable performance on incoverage input. In this paper, we compare the performance of an automatic speech recognition system using a grammar and a statistical language model including garbage models in the case of very limited in-domain training data. The results show that a bigram language model and a grammar show similar performance, and that the inclusion of garbage models in statistical language models enhances their performance both on in-coverage and out-of-coverage utterances." @default.
- W2400358286 created "2016-06-24" @default.
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- W2400358286 date "2005-09-04" @default.
- W2400358286 modified "2023-10-14" @default.
- W2400358286 title "Improving out-of-coverage language modelling in a multimodal dialogue system using small training sets" @default.
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- W2400358286 doi "https://doi.org/10.21437/interspeech.2005-404" @default.
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