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• W2078008248 abstract "Language modeling (LM), providing a principled mechanism to associate quantitative scores to sequences of words or tokens, has long been an interesting yet challenging problem in the field of speech and language processing. The n-gram model is still the predominant method, while a number of disparate LM methods, exploring either lexical co-occurrence or topic cues, have been developed to complement the n-gram model with some success. In this paper, we explore a novel language modeling framework built on top of the notion of relevance for speech recognition, where the relationship between a search history and the word being predicted is discovered through different granularities of semantic context for relevance modeling. Empirical experiments on a large vocabulary continuous speech recognition (LVCSR) task seem to demonstrate that the various language models deduced from our framework are very comparable to existing language models both in terms of perplexity and recognition error rate reductions." @default.
• W2078008248 created "2016-06-24" @default.
• W2078008248 creator A5053222355 @default.
• W2078008248 creator A5056367345 @default.
• W2078008248 date "2013-07-01" @default.
• W2078008248 modified "2023-10-17" @default.
• W2078008248 title "Leveraging relevance cues for language modeling in speech recognition" @default.
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• W2078008248 doi "https://doi.org/10.1016/j.ipm.2013.01.005" @default.
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