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- W2122888894 abstract "In spontaneous speech, speakers segment their speech into intonational phrases, and make repairs to what they are saying. However, techniques for understanding spontaneous speech tend to treat these events as noise, in the same manner as they handle out-of-grammar constructions and mis-recognitions. In our approach, we advocate that these events should be explicitly modeled, and that they must be resolved early in the processing stream. We put forward a statistical language model, which can be used during speech recognition, that models these events. This not only improves speech recognition perplexity and POS tagging, but also results in much richer output from the recognizer, with speech repairs resolved and intonational phrase boundaries identified. Syntactic and semantic processing can thus focus on dealing with out-of-grammar constructions and misrecognitions." @default.
- W2122888894 created "2016-06-24" @default.
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- W2122888894 date "2001-01-01" @default.
- W2122888894 modified "2023-09-27" @default.
- W2122888894 title "Improving Robustness by Modeling Spontaneous Speech Events" @default.
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- W2122888894 doi "https://doi.org/10.1007/978-94-015-9719-7_5" @default.
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