Parsing Spontaneous Dialogue

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Recent progress in the speech recognition field suggests great promise for practical spoken language interfaces within the next decade. But this technology is not yet ready for many applications because of the lack of effective methods for handling spontaneous speech. This project focuses on techniques that can provide additional information to a natural-language parser beyond the output of the speech recognition system. This extra information will be used in handling the following problems that arise in spontaneous dialogue: the segmentation of utterances into meaningful units, the identification and correction of speech repairs, the detection and correction of speech recognition errors, and the interpretation of the intended speech act from surface structure, lexical cues and intonation. The approach involves using stochastic techniques to identify likely recognition errors, speech repairs, utterance unit boundaries and prosodic features and then passing this information on to a parser which determines the best interpretation given all the information. The results of this project will help the development of accurate speech recognition systems for spontaneous dialogue. The techniques developed will be evaluated using a series of empirical tests on a corpus of human-human dialogues, and by integrating the techniques into a fully functional dialogue system.