Bi-modal sentence structure for language modeling

According to discourse theories in linguistics, conversational utterances p ossess an informational structure. That is, each sentence consists of two c omponents: the given and the new. The given refers to information that has previously been conveyed in the conversation such as that in That's interes ting. The new section of a sentence introduces additional information that is new to the conversation such as the word interesting in the previous exa mple. In this work, we take advantage of this inherent structure for the pu rpose of automatic conversational speech recognition by building sub-senten ce discourse language models (LMs) to represent the bi-modal nature of each conversational sentence. The internal sentence structure is captured with a statistical sentence model regardless of whether the input sentences are linguistically or acoustically segmented. The proposed model is verified on the Switchboard corpus. The resulting model contributes to a reduction in both LM perplexity and word recognition error rate. (C) 2000 Elsevier Scien ce B.V. All rights reserved.