Sub-sentence discourse models for conversational speech recognition
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference（1998）
摘要
According to discourse theories in linguistics, conversational utterances possess an informational structure that partitions each sentence into two portions: a “given” and “new”. We explore this idea by building sub-sentence discourse language models for conversational speech recognition. The internal sentence structure is captured in statistical language modeling by training multiple n-gram models using the expectation-maximization algorithm on the Switchboard corpus. The resulting model contributes to a 30% reduction in language model perplexity and a small gain in word error rate
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关键词
grammars,natural languages,speech processing,speech recognition,statistical analysis,Switchboard corpus,conversational speech recognition,conversational utterances,expectation-maximization algorithm,given-new language model,informational structure,internal sentence structure,language model perplexity reduction,linguistics,multiple n-gram models training,statistical language modeling,sub-sentence discourse models,word error rate
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