Using information above the word level for automatic speech recognition
This thesis introduces a general method for using information at the utterance level and across utterances for automatic speech recognition. The method involves classi cation of utterances into types. Using constraints at the utterance level via this classi cation method allows information sources to be exploited which cannot necessarily be used directly for word recognition. The classi cation power of three sources of information is investigated: the language model in the speech recogniser, dialogue context and intonation. The method is applied to a challenging task: the recognition of spontaneous dialogue speech. The results show success in automatic utterance type classi cation, and subsequent word error rate reduction over a baseline system, when all three information sources are probabilistically combined. iii