Prosodic features which cue back-channel responses in English and Japanese

Ward, Nigel; Tsukahara, Wataru

doi:10.1016/s0378-2166(99)00109-5

Cited by 303 publications

(288 citation statements)

References 21 publications

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“…The examples may seem trite, but micro-domains can be very useful for gathering data on how human-like features are received. Nigel Ward's humming machine (Ward & Tsukahara, 2000) is an example of a machine that can potentially make a human believe she is talking to another human, if presented in an appropriate context -like a telephone conversation where one person does most of the talking. Apart from the novelty value, however, such a machine goes a long way to test models of where to produce backchannels -the reason the machine was designed in the first place.…”

Section: Micro-domainsmentioning

confidence: 99%

Towards human-like spoken dialogue systems

Edlund¹,

Gustafson²,

Heldner³

et al. 2008

Speech Communication

106

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Section: Micro-domainsmentioning

confidence: 99%

Towards human-like spoken dialogue systems

Edlund¹,

Gustafson²,

Heldner³

et al. 2008

Speech Communication

106

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“…We noticed three phenomena. First, in some dialogs the operator seemed to actively solicit acknowledgements, prosodically (Ward & Tsukahara 2000), which seemed to drive the dialog faster. Second, in some dialogs the operator paused after every digit.…”

Section: Discussionmentioning

confidence: 99%

Automatic User-Adaptive Speaking Rate Selection

Ward¹,

Nakagawa²

2004

International Journal of Speech Technology

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Today there are many services which provide information over the phone using a prerecorded or synthesized voice. These voices are invariant in speed. Humans giving information over the telephone, however, tend to adapt the speed of their presentation to suit the needs of the listener. This paper presents a preliminary model of this adaptation.In a corpus of simulated directory assistance dialogs the operator's speed in number-giving correlates with the speed of the user's initial response and with the user's speaking rate.Multiple regression gives a formula which predicts appropriate speaking rates, and these predictions correlate (.46) with the speeds observed in good dialogs in the corpus. It is therefore easy, at least in principle, to make systems which adapt their speed to users' needs.

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“…These cues have been analysed extensively. It has been found that acoustic features [9,12,22], syntactic information [9,12], gaze [3], as well as head gestures [10] play a role in eliciting feedback responses from listeners. The mechanism used to identify feedback elicitation cues used in these studies, however, is problematic for two reasons.…”

Section: Feedback Elicitationmentioning

confidence: 99%

When to Elicit Feedback in Dialogue: Towards a Model Based on the Information Needs of Speakers

Buschmeier

Kopp

2014

Intelligent Virtual Agents

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Abstract. Communicative feedback in dialogue is an important mechanism that helps interlocutors coordinate their interaction. Listeners pro-actively provide feedback when they think that it is important for the speaker to know their mental state, and speakers pro-actively seek listener feedback when they need information on whether a listener perceived, understood or accepted their message. This paper presents first steps towards a model for enabling attentive speaker agents to determine when to elicit feedback based on continuous assessment of their information needs about a user's listening state.

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Prosodic features which cue back-channel responses in English and Japanese

Cited by 303 publications

References 21 publications

Towards human-like spoken dialogue systems

Towards human-like spoken dialogue systems

Automatic User-Adaptive Speaking Rate Selection

When to Elicit Feedback in Dialogue: Towards a Model Based on the Information Needs of Speakers

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