Spoken dialogue is increasingly central to systems that assist people. As the tasks that people and machines speak about together become more complex, however, users’ dissatisfaction with those systems is an important concern. This paper presents a novel approach to learning for spoken dialogue systems. It describes embedded wizardry, a methodology for learning from skilled people, and applies it to a library whose patrons order books by telephone. To address the challenges inherent in this application, we introduce RFW+, a domain-independent, feature-selection method that considers feature categories. Models learned with RFW+ on embedded-wizard data improve the performance of a traditional spoken dialogue system.

As people engage in increasingly complex conversations with computers, the need for generality and flexibility in spoken dialogue systems becomes more apparent. This pa­per describes how three different spoken dialogue systems for the same task reason with knowledge and certainty as they seek to understand what people want. It advocates sys­tems that exploit partial understanding, consider credibility, and are aware both of what they know and of their certainty that it matches their users’ intent.

Spoken language is an important and natural way for people to communicate with computers. Nonetheless, habitable, reliable, and efficient human-machine dialogue remains difficult to achieve. This paper describes a multi-threaded semi-synchronous architecture for spoken dialogue systems. The focus here is on its utterance interpretation module. Unlike most architectures for spoken dialogue systems, this new one is designed to be robust to noisy speech recognition through earlier reliance on context, a mixture of rationales for interpretation, and fine-grained use of confidence measures. We report here on a pilot study that demonstrates its robust understanding of users’ objectives, and we compare it with our earlier spoken dialogue system implemented in a traditional pipeline architecture. Substantial improvements appear at all tested levels of recognizer performance.

The social and collaborative nature of dialogue challenges A spoken dialogue system (SDS) has a social role: it supposedly an SDS in many ways. The spontaneity of dialogue gives allows people to communicate with a computer in rise to disfluencies, where a person repeats or interrupts ordinary language. A robust SDS should support coherent herself, produces filled pauses or false starts and selfrepairs. Disfluencies play a fundamental role in dialogue, and habitable dialogue, even when it confronts situations as signals for turn-taking (Gravano, 2009; Sacks, Schegloff for which it has no explicit pre-specified behavior. To ensure robust task completion, however, SDS designers typically and Jefferson, 1974) and for grounding to establish shared produce systems that make a sequence of rigid demands beliefs about the current state of mutual understanding on the user, and thereby lose any semblance of natural (Clark and Schaefer, 1989). Most SDSs handle the content dialogue. The thesis of our work is that a dialogue of the user's utterances, but do not fully integrate the way they address utterance meaning, disfluencies, turn-taking should evolve as a set of agreements that arise from joint and the collaborative nature of grounding.