Perception and prediction of speaker appeal – A single speaker study

Cullen, Ailbhe; Hines, Andrew; Harte, Naomi

doi:10.1016/j.csl.2018.04.004

Cited by 2 publications

(4 citation statements)

References 50 publications

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“…For each TIP item, the interrater reliability was calculated using a two-way, mixed, absolute, average-measures intraclass correlation coefficient (ICC) [21]. The results showed that among 22 items given in Table 2, 15 items at T1 (except items 4, 8, 9, 10, 15, 17, 22) and 14 items in T2 (except 5,7,8,9,10,17,20,22) exhibited ICCs above 0.60. High ICC value (> 0.60) indicates high interrater reliability and implies that the criteria rated similarly across raters.…”

Section: Youth Presents Presentation Competence Datasetmentioning

confidence: 99%

“…Speech Analysis-based NFs. Speech analysis is the most popular method to assess presentation performance [24,8,39,10]. We used the state-of-the-art acoustic features extraction tool, OpenSMILE [13], to obtain the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) [12], which constitutes 88 features related to the audio signal.…”

Section: Nonverbal Feature Extractionmentioning

confidence: 99%

“…Some studies in the social computing domain have therefore investigated automated assessment with regard to audio-based nonverbal features (NFs) [26,24,20], video-based NFs [33,8], or with a multimodal approach as in [39,40,15,8,7,25]. Related works that performed automated public speaking competence analysis indicate that there are different types of speeches such as scientific presentations [33,25,16,11], political speeches [31,10], and video interviews [26].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Presentation Competence using Multimodal Nonverbal Behavioral Cues

Sümer,

Beyan,

Ruth

et al. 2021

Preprint

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Public speaking and presentation competence plays an essential role in many areas of social interaction in our educational, professional, and everyday life. Since our intention during a speech can differ from what is actually understood by the audience, the ability to appropriately convey our message requires a complex set of skills. Presentation competence is cultivated in the early school years and continuously developed over time. One approach that can promote efficient development of presentation competence is the automated analysis of human behavior during a speech based on visual and audio features and machine learning. Furthermore, this analysis can be used to suggest improvements and the development of skills related to presentation competence. In this work, we investigate the contribution of different nonverbal behavioral cues, namely, facial, body pose-based, and audio-related features, to estimate presentation competence. The analyses were performed on videos of 251 students while the automated assessment is based on manual ratings according to the Tübingen Instrument for Presentation Competence (TIP). Our classification results reached the best performance with early fusion in the same dataset evaluation (accuracy of 71.25%) and late fusion of speech, face, and body pose features in the cross dataset evaluation (accuracy of 78.11%). Similarly, regression results performed the best with fusion strategies.

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Section: Youth Presents Presentation Competence Datasetmentioning

confidence: 99%

Section: Nonverbal Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Presentation Competence using Multimodal Nonverbal Behavioral Cues

Sümer,

Beyan,

Ruth

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Last but not least, variation of intensity, duration of speech, duration of pauses and pitch have been investigated in order to assess the voice. However, prosodic characteristics also become one of the pillars of the recognition of paralinguistic traits [6]. In this sense, with the use of Opensmile a set of features deemed relevant, based on the literature, e.g., [25], such as speech intensity, energy, pitch, jitter, loudness, and MFCCs and voice activity detection (VAD) [23], were extracted.…”

Section: Features Per Communication Channelmentioning

confidence: 99%

Understanding Public Speakers’ Performance: First Contributions to Support a Computational Approach

Barros

Conde

Soares

et al. 2020

Lecture Notes in Computer Science

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Communication is part of our everyday life and our ability to communicate can have a significant role in a variety of contexts in our personal, academic, and professional lives. For long, the characterization of what is a good communicator has been subject to research and debate by several areas, particularly in Education, with a focus on improving the performance of teachers. In this context, the literature suggests that the ability to communicate is not only defined by the verbal component, but also by a plethora of non-verbal contributions providing redundant or complementary information, and, sometimes, being the message itself. However, even though we can recognize a good or bad communicator, objectively, little is known about what aspects -and to what extent -define the quality of a presentation. The goal of this work is to create the grounds to support the study of the defining characteristics of a good communicator in a more systematic and objective form. To this end, we conceptualize and provide a first prototype for a computational approach to characterize the different elements that are involved in communication, from audiovisual data, illustrating the outcomes and applicability of the proposed methods on a video database of public speakers.

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Perception and prediction of speaker appeal – A single speaker study

Cited by 2 publications

References 50 publications

Estimating Presentation Competence using Multimodal Nonverbal Behavioral Cues

Estimating Presentation Competence using Multimodal Nonverbal Behavioral Cues

Understanding Public Speakers’ Performance: First Contributions to Support a Computational Approach

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