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Understanding in spoken dialogue systems

Xem 1-9 trên 9 kết quả Understanding in spoken dialogue systems
  • For spoken dialogue systems to correctly understand user intentions to achieve certain tasks while conversing with users, the dialogue state has to be appropriately updated (Zue and Glass, 2000) after each user utterance. Here, a dialogue state means all the information that the system possesses concerning the dialogue. For example, a dialogue state includes intention recognition results after each user utterance, the user utterance history, the system utterance history, and so forth.

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  • To tackle the problem of presenting a large number of options in spoken dialogue systems, we identify compelling options based on a model of user preferences, and present tradeoffs between alternative options explicitly. Multiple attractive options are structured such that the user can gradually refine her request to find the optimal tradeoff. We show that our approach presents complex tradeoffs understandably, increases overall user satisfaction, and significantly improves the user’s overview of the available options.

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  • We present a data-driven approach to learn user-adaptive referring expression generation (REG) policies for spoken dialogue systems. Referring expressions can be difficult to understand in technical domains where users may not know the technical ‘jargon’ names of the domain entities. In such cases, dialogue systems must be able to model the user’s (lexical) domain knowledge and use appropriate referring expressions.

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  • Over several years, we have developed an approach to spoken dialogue systems that includes rule-based and trainable dialogue managers, spoken language understanding and generation modules, and a comprehensive dialogue system architecture. We present a Reinforcement Learning-based dialogue system that goes beyond standard rule-based models and computes on-line decisions of the best dialogue moves. The key concept of this work is that we bridge the gap between manually written dialog models (e.g.

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  • An increasing number of telephone services are offered in a fully automatic way with the help of speech technology. The underlying systems, called spoken dialogue systems (SDSs), possess speech recognition, speech understanding, dialogue management, and speech generation capabilities, and enable a more-or-less natural spoken interaction with the human user. Nevertheless, the principles underlying this type of interaction are different from the ones which govern telephone conversations between humans, because of the limitations of the machine interaction partner.

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  • This paper proposes a method for incrementally understanding user utterances whose semantic boundaries are not known and responding in real time even before boundaries are determined. It is an integrated parsing and discourse processing method that updates the partial result of understanding word by word, enabling responses based on the partial result. This method incrementally finds plausible sequences of utterances that play crucial roles in the task execution of dialogues, and utilizes beam search to deal with the ambiguity of boundaries as well as syntactic and semantic ambiguities. ...

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  • Speech recognition problems are a reality in current spoken dialogue systems. In order to better understand these phenomena, we study dependencies between speech recognition problems and several higher level dialogue factors that define our notion of student state: frustration/anger, certainty and correctness. We apply Chi Square (χ2) analysis to a corpus of speech-based computer tutoring dialogues to discover these dependencies both within and across turns.

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  • We describe a domain-independent semantic interpretation architecture suitable for spoken dialogue systems, which uses a decision-list method to effect a transparent combination of rule-based and data-driven approaches. The architecture has been implemented and evaluated in the context of a mediumvocabulary command and control task.

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  • II. THE STUDY ABSTRACT A desirable long-range goal in building future speech understanding systems would be to accept the kind of language people spontaneously produce. We show that people do not speak to one another in the same way they converse in typewritten language. Spoken language is finer-grained and more indirect. The differences are striking and pervasive. Current techniques for engaging in typewritten dialogue will need to be extended to accomodate the structure of spoken language.

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