N-Best List Reranking using Higher Level Phonetic, Lexical, Syntactic and Semantic Knowledge Sources

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 focuses 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 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