DUEL

Although disfluent speech is pervasive in spoken conversation, disfluencies have received little attention within formal

theories of grammar---they are widely perceived as meaning-free production errors. The majority of work on disfluent language has

come from psycholinguistic models of speech production and comprehension and from structural approaches designed to

improve performance in speech applications. Over recent years much evidence has accumulated that disfluencies, far

from being meaningless noise, contain much useful information that guides language users's actions and evaluations of

their interlocuters' states of mind. Moreover, they exhibit rule-like regularities on all levels (including phonology, syntax,

and semantics.). In DUEL we aim to show how disfluent speech across a number of languages (including

French, German, English, and Chinese) can be analyzed in a precise way on the basis of formal grammatical tools, using

this theory to guide the design of dialogue systems which can deal head on with disfluent speech, exploiting the information

therein rather than filtering it away. DUEL will also tackle another phenomenon that has not hitherto received attention

from formal grammarians, namely laughter. Empirical studies over recent years have shown that these occur relatively

frequently in conversation and do not typically involve `humorous' utterances. They play a significant semantic role, e.g. in

indicating an utterance is not to be taken seriously or in enabling a socially delicate utterance to be made without causing

offence. Our aim is to develop precise analyses of how laughter is integrated in the emergence of meaning, precise

enough to enable dialogue systems that understand and respond to laughter to be implemented. The tools developed in

DUEL to analyze disfluency and laughter will enable a variety of other dialogical phenomena that have been somewhat

marginal to be analyzed, e,g, exclamations, tag questions, and corrective particles such as `No'. Both theory and

implementation in DUEL will draw on carefully collected parallel data in French, German, and Chinese, as well as a small

number of experimental studies. This will enable subtle cross-linguistic

and cross-cultural differences to be described, as well as deeper commonalities to be hypothesized.