Automatic utterance type detection using suprasegmental features

The goal of the work presented here is to automatically predict the type of an utterance in spoken dialogue by using automatically extracted suprasegmental information. For this task, we present and compare three stochastic algorithms: hidden Markov models, artificial neural nets, and classification and regression trees. These models are easily trainable, reasonably robust and fit into the probabilistic framework required for speech recognition. Utterance type detection is dependent on the assumption that different types of utterances have different suprasegmental characteristics. The categorisation of utterance types is based on the theory of conversation games and consists of 12 move types (e.g. reply to a question, wh-question, acknowledgement). This utterance type detector is used in an automatic speech recognition system to reduce the word error rate.