The ALICO corpus: analysing the active listener

Malisz, Zofia; Włodarczak, Marcin; Buschmeier, Hendrik; Skubisz, Joanna; Kopp, Stefan; Wagner, Petra

doi:10.1007/s10579-016-9355-6

Cited by 16 publications

(19 citation statements)

References 42 publications

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“…Like Malisz et al (2016), we also found that nods more commonly occurred in groups than one-by-one, see Table 2. Malisz et al (2016) also found the same pattern for head shakes, which we do not significantly see in our corpus.…”

Section: What Modalities Are Most Commonly Used To Convey Negative and Positive Feedback?supporting

confidence: 59%

“…In a multimodal study of the ALICO corpus, Malisz et al (2016) found that listeners used head movements twice as often as speech in response to being told a story by a speaker. Additionally, nods are by far the most common head movement feature, and multiple nods are twice as common as single nods.…”

Section: Head Movementsmentioning

confidence: 99%

“…Another annotator may have seen other clips of the robot eliciting feedback from users, and assume that the robot always wants the listener to react in some way, and thus evaluates the same clip as negative. For comparison, Malisz et al (2016), who classified dialogues using four feedback levels similar to the schemes described in section 2.2, achieved a κ score of around 0.3.…”

Section: Feedback Polarity Of Each Clipmentioning

confidence: 99%

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Multimodal User Feedback During Adaptive Robot-Human Presentations

Axelsson¹,

Skantze²

2022

Front. Comput. Sci.

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Feedback is an essential part of all communication, and agents communicating with humans must be able to both give and receive feedback in order to ensure mutual understanding. In this paper, we analyse multimodal feedback given by humans towards a robot that is presenting a piece of art in a shared environment, similar to a museum setting. The data analysed contains both video and audio recordings of 28 participants, and the data has been richly annotated both in terms of multimodal cues (speech, gaze, head gestures, facial expressions, and body pose), as well as the polarity of any feedback (negative, positive, or neutral). We train statistical and machine learning models on the dataset, and find that random forest models and multinomial regression models perform well on predicting the polarity of the participants' reactions. An analysis of the different modalities shows that most information is found in the participants' speech and head gestures, while much less information is found in their facial expressions, body pose and gaze. An analysis of the timing of the feedback shows that most feedback is given when the robot makes pauses (and thereby invites feedback), but that the more exact timing of the feedback does not affect its meaning.

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Section: What Modalities Are Most Commonly Used To Convey Negative and Positive Feedback?supporting

confidence: 59%

Section: Head Movementsmentioning

confidence: 99%

Section: Feedback Polarity Of Each Clipmentioning

confidence: 99%

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Multimodal User Feedback During Adaptive Robot-Human Presentations

Axelsson¹,

Skantze²

2022

Front. Comput. Sci.

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“…Adult-adult interactions have been extensively studied in the ALICO and MultiLis corpora. ALICO aimed to capture the spoken and gestural dynamics of storyteller-listener dialogue [23]. MultiLis focused on identifying individual differences and similarities of listener responses by having three listeners simultaneously interact with the same speaker [11].…”

Section: Applications In Educational Interactive Technologiesmentioning

confidence: 99%

P2pstory

Singh

Lee

Grover

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Understanding social-emotional behaviors in storytelling interactions plays a critical role in the development of interactive educational technologies for children. A challenge when designing for such interactions using technology like social robots, virtual agents, and tablets is understanding the social-emotional behaviors pertinent to storytelling-especially when emulating a natural peer-to-peer relation between the child and the technology. We present P2PSTORY, a dataset of young children (5-6 years old) engaging in natural peerto-peer storytelling interactions with fellow classmates. The dataset consists of rich social behaviors of children without adult supervision, with each participant demonstrating being a storyteller and a listener. The dataset contains 58 video recorded sessions along with a diverse set of behavioral annotations as well as developmental and demographic profiles of each child participant. We describe the main characteristics of the dataset in addition to findings that reveal perceptual differences between adults and children when evaluating the attentiveness of listeners.

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“…Body movements have been cited as contributing to the coordinated process of social collaboration in a variety of ways. Turn-taking research indicates that people deploy a broad scope of body movements to yield or take the floor, such as pointing gestures (Goodwin 2000; Mondada 2007; Sikveland and Ogden 2012), head movements (Cerrato and Skhiri 2003; Duncan 1972; Hadar et al 1985; Malisz et al 2016; Rahayudi et al 2014), eye gaze (Bavelas et al 2002; Bavelas 2005; Brône et al 2013; Jokinen 2009; Peters et al 2005), prevocal preparations like mouth openings (Streeck and Hartge 1992), and body posture (Holler and Kendrick 2015). Beyond turn-taking, evidence shows that body movements when coupled with speech (co-verbal gestures, such as iconic and other representational gestures; Mittelberg and Evola 2014) provide information not present at the speech level which is successfully decoded by the observer–listener (Kendon 2015; McNeill 1992), even during speech-gesture mismatches (McNeill et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Coordinated Collaboration and Nonverbal Social Interactions: A Formal and Functional Analysis of Gaze, Gestures, and Other Body Movements in a Contemporary Dance Improvisation Performance

Evola

Skubisz

2019

J Nonverbal Behav

Self Cite

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This study presents a microanalysis of what information performers “give” and “give off” to each other via their bodies during a contemporary dance improvisation. We compare what expert performers and non-performers (sufficiently trained to successfully perform) do with their bodies during a silent, multiparty improvisation exercise, in order to identify any differences and to provide insight into nonverbal communication in a less conventional setting. The coordinated collaboration of the participants (two groups of six) was examined in a frame-by-frame analysis focusing on all body movements, including gaze shifts as well as the formal and functional movement units produced in the head–face, upper-, and lower-body regions. The Methods section describes in detail the annotation process and inter-rater agreement. The results of this study indicate that expert performers during the improvisation are in “performance mode” and have embodied other social cognitive strategies and skills (e.g., endogenous orienting, gaze avoidance, greater motor control) that the non-performers do not have available. Expert performers avoid using intentional communication, relying on information to be inferentially communicated in order to coordinate collaboratively, with silence and stillness being construed as meaningful in that social practice and context. The information that expert performers produce is quantitatively less (i.e., producing fewer body movements) and qualitatively more inferential than intentional compared to a control group of non-performers, which affects the quality of the performance.

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The ALICO corpus: analysing the active listener

Cited by 16 publications

References 42 publications

Multimodal User Feedback During Adaptive Robot-Human Presentations

Multimodal User Feedback During Adaptive Robot-Human Presentations

P2pstory

Coordinated Collaboration and Nonverbal Social Interactions: A Formal and Functional Analysis of Gaze, Gestures, and Other Body Movements in a Contemporary Dance Improvisation Performance

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