Observations on overlap: findings and implications for automatic processing of multi-party conversation

Shriberg, Elizabeth; Stolcke, Andreas; Baron, Don

doi:10.21437/eurospeech.2001-352

Cited by 115 publications

(17 citation statements)

References 6 publications

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“…Our frequencies suggest a similar tendency, since the PR flirts with the threshold at 1.9. The same PR occurs with increases in volume of more than 5dB with turn-competitive overlapping reactions, which was also reported by Wells & McFarlane (1998) and Kurtić et al (2013), (see also French & Local 1983, Shriberg et al 2001, Schegloff 2000.…”

Section: Prosodic Featuressupporting

confidence: 81%

“…Based on the relative frequencies we can conclude that these acknowledgements carry falls in the majority of cases, though there is no significant distributional difference compared to other functions such as the non-committals and the turn-competitive overlaps. Thirdly, French & Local (1983), Shriberg et al (2001), Schegloff (2000) and Kurtić et al (2013) report an increase in pitch with turn-competitive overlaps. Our frequencies suggest a similar tendency, since the PR flirts with the threshold at 1.9.…”

Section: Prosodic Featuresmentioning

confidence: 97%

See 1 more Smart Citation

Overlapping Reactions During Tag Questions

Kimps

O'Grady

2022

WORD</i>

1
0
0
0

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Section: Prosodic Featuressupporting
confidence: 81%

Section: Prosodic Featuresmentioning
confidence: 97%

Overlapping Reactions During Tag Questions
Kimps
¹
,
O'Grady
²

2022
WORD</i>
1
0
0
0
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“…When the interactions are not positive, interruptions and other behaviours related to aggressivity and dominance appear more frequently [66]. Note, however, that the amount of overlapping speech accounts for up to 10% of the total time even in normal conversations [63].…”

Section: Managing Interactionmentioning
confidence: 99%

Social signals, their function, and automatic analysis
Vinciarelli
¹
,
Pantić
²
,
Bourlard
³

et al. 2008
Proceedings of the 10th International Conference on Multimodal Interfaces
69
2
42
0
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Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitudes towards other human (and virtual) participants in the current social context. As such, SSP integrates both engineering (speech analysis, computer vision, etc.) and human sciences (social psychology, anthropology, etc.) as it requires multimodal and multidisciplinary approaches. As of today, SSP is still in its early infancy, but the domain is quickly developing, and a growing number of works is appearing in the literature. This paper provides an introduction to nonverbal behaviour involved in social signals and a survey of the main results obtained so far in SSP. It also outlines possibilities and challenges that SSP is expected to face in the next years if it is to reach its full maturity.

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“…Most of the studies indicate the importance of prosodic features such as fundamentalfrequency (F0) or intensity during speaking turn exchanges [23,39,54]. Studies found that people raise their energy and voice when they attempt to interrupt the current speaker [50,26]. Hammarberg [27] provided similar evidence regarding pitch and amplitude.…”

Section: Characterization Of Speaking Turn Exchangesmentioning
confidence: 99%

Multimodal Analysis of Interruptions
Liu
¹
,
Achard
²
,
Pélachaud
³

2022
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and C
1
0
0
0
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During an interaction, interactants exchange speaking turns. Exchanges can be done smoothly or through interruptions. Listeners can display backchannels, send signals to grab the speaking turn, wait for the speaker to yield the turn, or even interrupt and grab the speaking turn. Interruptions are very frequent in natural interactions. To create believable and engaging interaction between human interactants and embodied conversational agent ECA, it is important to endow virtual agent with the capability to manage interruptions, that is to have the ability to interrupt, but also to react to an interruption. As a first step, we focus on the later one where the agent is able to perceive and interpret the user's multimodal behaviors as either an attempt or not to take the turn. To this aim, we annotate, analyse and characterize interruptions in humanhuman conversations. In this paper, we describe our annotation schema that embeds different types of interruptions. We then provide an analysis of multimodal features, focusing of prosodic features (F0 and loudness) and body (head and hand) activity, to characterize interruptions.

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Observations on overlap: findings and implications for automatic processing of multi-party conversation

Cited by 115 publications

References 6 publications

Overlapping Reactions During Tag Questions

Overlapping Reactions During Tag Questions

Social signals, their function, and automatic analysis

Multimodal Analysis of Interruptions

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