Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities

This paper presents a dialogue act classification for a spoken dialogue system that delivers necessary information to elderly subjects with mild dementia. Lexical features have been shown to be effective for classification, but the automatic transcription of spontaneous speech demands expensive language modeling. Therefore, this paper proposes a classifier that does not require language modeling and that uses sub-lexical features instead of lexical features. This classifier operates on sequences of phonemes obtained by a phoneme recognizer and exhaustively analyzes the saliency of all possible sub-sequences using a support vector machine with a string kernel. An empirical study of a dialogue corpus containing elderly speech showed that the sub-lexical classifier was robust against the poor modeling of language and it performed better than a lexical classifier that used hidden Markov models of words. Index Terms: dialogue acts, support vector machines, string kernels, spontaneous speech, elderly speech, dementia