A prosodic feature that invites back-channels in Egyptian Arabic

Ward, Nigel; Bayyari, Yaffa Al

doi:10.1075/cilt.290.16war

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…In [16], it was reported that increases in pitch and intensity, as well as certain POS patterns, are key back-channel-inviting cues in taskoriented dialog. The multi-lingual comparison discussed in [5,6,7] found that pitch patterns, e.g., periods of low pitch or drops in pitch, are positively associated with listener back-channels in Japanese-, English-, Arabic-and Spanish-speakers. Recently, [17] investigated cultural differences in gaze, proxemics, and back-channel behavior in a multi-modal corpus of American English, Mexican Spanish, and Arabic speakers.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work has identified cross-cultural differences in a variety of behaviors that may play a role in signaling mutual engagement, endorsement or appreciation. These behaviors include nodding [1], posture [2], facial expression [3], gaze [4], cues to vocal back-channel [5,6,7], nonverbal back-channel [8]), and coverbal gesturing [9] as well.…”

Section: Introductionmentioning

confidence: 99%

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Employing boosting to compare cues to verbal feedback in multi-lingual dialog

Levow

Wang

2012

2012 IEEE Spoken Language Technology Workshop (SLT)

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Verbal feedback provides important cues in establishing interactional rapport. The challenge of recognizing contexts for verbal feedback largely arises from relative sparseness and optionality. In addition, cross-language and inter-speaker variations can make recognition more difficult. In this paper, we show that boosting can improve accuracy in recognizing contexts for verbal feedback based on prosodic cues. In our experiments, we use dyads from three languages (English, Spanish and Arabic) to evaluate two boosting methods, generalized Adaboost and Gradient Boosting Trees, against Support Vector Machines (SVMs) and a naive baseline, with explicit oversampling on the minority verbal feedback instances. We find that both boosting methods outperform the baseline and SVM classifiers. Analysis of the feature weighting by the boosted classifiers highlights differences and similarities in the prosodic cues employed by members of these diverse language/cultural groups.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Employing boosting to compare cues to verbal feedback in multi-lingual dialog

Levow

Wang

2012

2012 IEEE Spoken Language Technology Workshop (SLT)

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“…Yet, related research on gaze so far has been mainly concerned with the analysis and modeling of dyadic interaction rather than multi-party interaction and has not focused on the role of the listener in particular (Argyle and Cook, 1976;Vertegaal et al, 2001;Vinayagamoorthy et al, 2004;Jaffe et al, 1973;Ruhland et al, 2015;Andrist et al, 2017;Mutlu et al, 2006). Similarily, also research on feedback token has mainly focused on analyzing dyadic interactions e.g., (Kawahara et al, 2016;Ward et al, 2007) and on modeling the timing of feedback token therein (Morency et al, 2010). Which modality should be chosen, audio or visual, and how feedback tokens are combined with gaze has not been the focus of research so far.…”

Section: Introductionmentioning

confidence: 99%

Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions

et al. 2021

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Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the human-human dialogue, are also beneficial for the perception of a robot in multi-party human-robot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant’s perception of the robot, his behavior as well as the perception of third-party observers.

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“…Accordingly, he suggested a well-made system, which produces a back channel item after a low pitch region of a certain frequency. Ward & Al Bayyari (2007) have also introduced various prosodic features in the speakers' speech signaling the appropriate times of back channeling in Egyptian Arabic, including a pitch upturn at the phrasal end, low flat pitch associated with a lengthened vowel at dis-fluency points, and a sharp pitch downslope.…”

Section: Linguistic Discovery 102:109-118mentioning

confidence: 99%