Backchannels: Quantity, Type and Timing Matters

Poppe, Ronald; Truong, Khiet P.; Heylen, Dirk

doi:10.1007/978-3-642-23974-8_25

Cited by 30 publications

(30 citation statements)

References 26 publications

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“…In that study and a number of followup studies, it was found that a strategy that just copies the timings of the original listener is often perceived as more natural than a strategy based on hand-designed rules [26,27]. The studies also suggest that random backcanneling according to an Erlang distribution achieves a rather good perceptual naturalness rating from human observers.…”

Section: Backchanneling For Embodied Conversational Agentsmentioning

confidence: 86%

Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent

Inden

Malisz

Wagner

et al. 2013

Proceedings of the 15th ACM on International Conference on Multimodal Interaction

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We report on an analysis of feedback behavior in an Active Listening Corpus as produced verbally, visually (head movement) and bimodally. The behavior is modeled in an embodied conversational agent and displayed in a conversation with a real human to human participants for perceptual evaluation. Five strategies for the timing of backchannels are compared: copying the timing of the original human listener, producing backchannels at randomly selected times, producing backchannels according to high level timing distributions relative to the interlocutor's utterance and pauses, or according to local entrainment to the interlocutors' vowels, or according to both. Human observers judge that models with global timing distributions miss less opportunities for backchanneling than random timing.

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Section: Backchanneling For Embodied Conversational Agentsmentioning

confidence: 86%

Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent

Inden

Malisz

Wagner

et al. 2013

Proceedings of the 15th ACM on International Conference on Multimodal Interaction

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show abstract

“…The time delay is understandable and inevitable as cognition delay. We compared the starting time of PCS-collected backchannels with the original ones to find out the time delay to be approximately 200ms, which is the same as the delay proposed by Poppe et al when they analyzed the influence of quantity, type and timing for backchannel generation of virtual listeners [24]. The final PCS data are obtained by eliminating 200ms delay from the collected clicking time.…”

Section: Data Collection With the Pcs Methodsmentioning

confidence: 99%

Backchannel Prediction for Mandarin Human-Computer Interaction

Mao

Peng

Xue

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn recent years, researchers have tried to create unhindered human-computer interaction by giving virtual agents human-like conversational skills. Predicting backchannel feedback for agent listeners has become a novel research hot-spot. The main goal of this paper is to identify appropriate features and methods for backchannel prediction in Mandarin conversations. Firstly, multimodal Mandarin conversations are recorded for the analysis of backchannel behaviors. In order to eliminate individual difference in the original face-to-face conversations, more backchannels from different listeners are gathered together. These data confirm that backchannels occurring in the speakers' pauses form a vast majority in Mandarin conversations. Both prosodic and visual features are used in backchannel prediction. Four types of models based on the speakers' pauses are built by using support vector machine classifiers. An evaluation of the pause-based prediction model has shown relatively high accuracy in consideration of the optional nature of backchannel feedback. Finally, the results of the subjective evaluation validate that the conversations performed between humans and virtual listeners using backchannels predicted by the proposed models is more unhindered compared to other backchannel prediction methods. key words : human-computer interaction, virtual agent, backchannel, Mandarin, support vector machine

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“…Bavelas et al [10] found that periods of mutual gaze increased the likelihood of a backchannel occurring. In [13], the effect of quantity, timing and type of backchannel was investigated. Participants were asked to rate whether the reaction of an artificial listener to a real speaker was human-like.…”

Section: Introductionmentioning

confidence: 99%

The Face Speaks: Contextual and Temporal Sensitivity to Backchannel Responses

Aubrey

Cunningham

Marshall

et al. 2013

Computer Vision - ACCV 2012 Workshops

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Abstract. It is often assumed that one person in a conversation is active (the speaker) and the rest passive (the listeners). Conversational analysis has shown, however, that listeners take an active part in the conversation, providing feedback signals that can control conversational flow. The face plays a vital role in these backchannel responses. A deeper understanding of facial backchannel signals is crucial for many applications in social signal processing, including automatic modeling and analysis of conversations, or in the development of life-like, effective conversational agents. Here, we present results from two experiments testing the sensitivity to the context and the timing of backchannel responses. We utilised sequences from a newly recorded database of 5-minute, two-person conversations. Experiment 1 tested how well participants would be able to match backchannel sequences to their corresponding speaker sequence. On average, participants performed well above chance. Experiment 2 tested how sensitive participants would be to temporal misalignments of the backchannel sequence. Interestingly, participants were able to estimate the correct temporal alignment for the sequence pairs. Taken together, our results show that human conversational skills are highly tuned both towards context and temporal alignment, showing the need for accurate modeling of conversations in social signal processing.

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Backchannels: Quantity, Type and Timing Matters

Cited by 30 publications

References 26 publications

Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent

Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent

Backchannel Prediction for Mandarin Human-Computer Interaction

The Face Speaks: Contextual and Temporal Sensitivity to Backchannel Responses

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