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- W2116765494 abstract "This paper presents a novel methodology to improve Large Vocabulary Continuous Speech Recognizer (LVCSR) hypotheses using additional phonetic, lexical, syntactic and semantic knowledge. Such additional higher level knowledge sources are unavailable during the LVCSR decoding due to the various constraints placed on the successful deployment of such information sources. This paper will focus on the extraction of WER improvements from the LVCSR n-best list using the additional higher level knowledge sources as the nucleus of a reranking mechanism. We shall illustrate the improvements obtained for the conversational speech transcription task and also for the directed dialog speech utterance transcription task in a grammar tuning application." @default.
- W2116765494 created "2016-06-24" @default.
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- W2116765494 date "2006-08-03" @default.
- W2116765494 modified "2023-09-27" @default.
- W2116765494 title "N-Best List Reranking using Higher Level Phonetic, Lexical, Syntactic and Semantic Knowledge Sources" @default.
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- W2116765494 doi "https://doi.org/10.1109/icassp.2006.1660045" @default.
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