Laughter Research: A Review of the ILHAIRE Project

Dupont, Stéphane; Çakmak, Hüseyin; Curran, William J.; Dutoit, Thierry; Hofmann, Jennifer; McKeown, Gary; Pietquin, Olivier; Platt, Tracey; Ruch, Willibald; Urbain, Jérôme

doi:10.1007/978-3-319-31056-5_9

Cited by 13 publications

(8 citation statements)

References 101 publications

(112 reference statements)

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“…Yet, research in this topic is growing, including large-scale interdisciplinary projects, such as the ILHAIRE project (ILHAIRE project, European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 270780, see [106] [107]).…”

Section: Resultsmentioning

confidence: 99%

Laughter and Smiling in 16 Positive Emotions

Hofmann

Platt

Ruch

2017

IEEE Trans. Affective Comput.

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This study investigated the elicitation of smiling and laughter and the role of facial display regulation markers (e.g., down-regulating of a smile or laugh) in positive emotions. In a structured group conversation setting, the frequency and intensity of Duchenne and non-Duchenne smiles and laughs when telling memories of 16 positive emotions proposed by Ekman [1] were assessed. Facial responses were coded with the Facial Action Coding System (FACS [2]) and laughter vocalizations were assessed. The results show that smiles and laughs occurred in all 16 positive emotions. Laughter occurred most often in amusement and schadenfreude (laughter occurred in 72% and 71% of the recalled emotion memories respectively). Also, the intensity of the smiles and laughs was higher in amusement and schadenfreude than in the other 14 positive emotions. Furthermore, down-regulated displays (i.e., including facial markers counteracting the upward action of the zygomatic major muscle) resembled Duchenne Displays in their intensity. To summarize, more insight is gained into the facial expression of positive emotions, also highlighting the role of laughter. Also, the importance of assessing regulation markers in joy displays when people are in social settings is stressed

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Section: Resultsmentioning

confidence: 99%

Laughter and Smiling in 16 Positive Emotions

Hofmann

Platt

Ruch

2017

IEEE Trans. Affective Comput.

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“…We hope that the collection will thus reflect the fact that laughter can interrupt or intermingle with speech [25,34,43], or happen at the extremities of sentences [37]. Several laughter databases can be found related to isolated laughter [10], but we know of no speech-laugh database suitable for machine learning-based work or analysis. AmuS will facilitate research into the use of these components in amused speech and their relation with amused speech arousal/intensity, and also into the smile-laughter continuum.…”

Section: Smiled Speechmentioning

confidence: 99%

Introducing AmuS: The Amused Speech Database

Haddad

Torre

Gilmartin

et al. 2017

Statistical Language and Speech Processing

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In this paper we present the AmuS database of about three hours worth of data related to amused speech recorded from two males and one female subjects and contains data in two languages French and English. We review previous work on smiled speech and speech-laughs. We describe acoustic analysis on part of our database, and a perception test comparing speech-laughs with smiled and neutral speech. We show the efficiency of the data in AmuS for synthesis of amused speech by training HMM-based models for neutral and smiled speech for each voice and comparing them using an on-line CMOS test.

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“…Laughter commonly occurs in daily interactions, not only simply in relation to funny situations, but also to some type of attitudes (like friendliness or interest), having an important social function in human-human communication [1,2], as well as positive influences on human's health [3,4]. Laughter also provides social signals that allow our conversations to flow smoothly among topics; to help us repairing conversations that are breaking down; and to finish our conversations on a positive note [5,6]. Thus, it is important to account for laughter in robot-mediated communication as well.…”

Section: Introductionmentioning

confidence: 99%

Analysis and generation of laughter motions, and evaluation in an android robot

Ishi¹,

Minato²,

Ishiguro³

2019

SIP

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Laughter commonly occurs in daily interactions, and is not only simply related to funny situations, but also to expressing some type of attitudes, having important social functions in communication. The background of the present work is to generate natural motions in a humanoid robot, so that miscommunication might be caused if there is mismatching between audio and visual modalities, especially in laughter events. In the present work, we used a multimodal dialogue database, and analyzed facial, head, and body motion during laughing speech. Based on the analysis results of human behaviors during laughing speech, we proposed a motion generation method given the speech signal and the laughing speech intervals. Subjective experiments were conducted using our android robot by generating five different motion types, considering several modalities. Evaluation results showed the effectiveness of controlling different parts of the face, head, and upper body (eyelid narrowing, lip corner/cheek raising, eye blinking, head motion, and upper body motion control).

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Laughter Research: A Review of the ILHAIRE Project

Cited by 13 publications

References 101 publications

Laughter and Smiling in 16 Positive Emotions

Laughter and Smiling in 16 Positive Emotions

Introducing AmuS: The Amused Speech Database

Analysis and generation of laughter motions, and evaluation in an android robot

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