A general, abstract model of incremental dialogue processing

Schlangen, David; Skantze, Gabriel

doi:10.3115/1609067.1609146

Cited by 104 publications

(111 citation statements)

References 24 publications

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“…The NUMBERS system presented in this paper can be seen as a specific instance (with some simplifying assumptions) of a more general, abstract model that we have developed (Schlangen & Skantze, 2009). We will here only briefly describe the parts of the general model that are relevant for the exposition of our system.…”

Section: A General Abstract Modelmentioning

confidence: 99%

Incremental dialogue processing in a micro-domain

Skantze

Schlangen

2009

Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09

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This paper describes a fully incremental dialogue system that can engage in dialogues in a simple domain, number dictation. Because it uses incremental speech recognition and prosodic analysis, the system can give rapid feedback as the user is speaking, with a very short latency of around 200ms. Because it uses incremental speech synthesis and self-monitoring, the system can react to feedback from the user as the system is speaking. A comparative evaluation shows that naïve users preferred this system over a non-incremental version, and that it was perceived as more human-like.

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Section: A General Abstract Modelmentioning

confidence: 99%

Incremental dialogue processing in a micro-domain

Skantze

Schlangen

2009

Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09

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“…Recent work in dialogue systems has moved away from using a rigid model of turn-taking and towards using incremental ASR results to determine the best system action before the user has finished speaking [3,4,5]. Partial, or incremental, ASR results are periodically obtained while the user is speaking, and the dialogue manager decides whether to act on these partial hypotheses or not, based on all available information.…”

Section: Incremental Dialoguementioning

confidence: 99%

Continuous asr for flexible incremental dialogue

Breslin

Gašić

Henderson

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Spoken dialogue systems provide a convenient way for users to interact with a machine using only speech. However, they often rely on a rigid turn taking regime in which a voice activity detection module is used to determine when the user is speaking and decide when is an appropriate time for the system to respond. This paper investigates replacing the VAD and discrete utterance recogniser of a conventional turn-taking system with a continuously operating recogniser that is always listening, and using the recogniser 1-best path to guide turn taking. In this way, a flexible framework for incremental dialogue management is possible. Experimental results show that it is possible to remove the VAD component and successfully use the recogniser best path to identify user speech, with more robustness to noise, potentially smaller latency times, and a reduction in overall recognition error rate compared to using the conventional approach.

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“…It is increasingly acknowledged that, to achieve a more natural dialog, social agents require incremental (dialog) processing [7]. Such incremental processing enables social agents to exhibit interpersonal coordination strategies such as backchannel feedback, smooth turn-taking, or an alignment of movement rhythm between interlocutors.…”

Section: Related Workmentioning

confidence: 99%

An Incremental Multimodal Realizer for Behavior Co-Articulation and Coordination

Welbergen

Reidsma

Kopp

2012

Intelligent Virtual Agents

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Abstract. Human conversations are highly dynamic, responsive interactions. To enter into flexible interactions with humans, a conversational agent must be capable of fluent incremental behavior generation. New utterance content must be integrated seamlessly with ongoing behavior, requiring dynamic application of co-articulation. The timing and shape of the agent's behavior must be adapted on-the-fly to the interlocutor, resulting in natural interpersonal coordination. We present AsapRealizer, a BML 1.0 behavior realizer that achieves these capabilities by building upon, and extending, two state of the art existing realizers, as the result of a collaboration between two research groups.

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A general, abstract model of incremental dialogue processing

Cited by 104 publications

References 24 publications

Incremental dialogue processing in a micro-domain

Incremental dialogue processing in a micro-domain

Continuous asr for flexible incremental dialogue

An Incremental Multimodal Realizer for Behavior Co-Articulation and Coordination

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