Discovering Emotions in Filipino Laughter Using Audio Features

Miranda, Miguel Cristobal; Alonzo, Julie; Campita, Janelle Marie; Lucila, Stephanie; Suarez, Merlin Teodosia

doi:10.1109/humancom.2010.5563313

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Different machine learning strategies vary in their success rates for classification of laughter types. A study on detecting emotions in Filipino laughter found that Multilayer Perceptron (MLP) yielded a higher correct classification rate (at 44%) compared with using SVM (18%) [ 73 ]. MLP considers the weights within a network to select features, and may be better suited for audio datasets, while SVM may perform better for video in cases where multimodal information is available [ 74 ].…”

Section: Computational Approachesmentioning

confidence: 99%

Laughter and culture

Bryant

Bainbridge

2022

Phil. Trans. R. Soc. B

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Like most human non-verbal vocalizations, laughter is produced by speakers of all languages, across all known societies. But despite this obvious fact (or perhaps because of it), there is little comparative research examining the structural and functional similarity of laughter across speakers from different cultures. Here, we describe existing research examining (i) the perception of laughter across disparate cultures, (ii) conversation analysis examining how laughter manifests itself during discourse across different languages, and (iii) computational methods developed for automatically detecting laughter in spoken language databases. Together, these three areas of investigation provide clues regarding universals and cultural variations in laughter production and perception, and offer methodological tools that can be useful for future large-scale cross-cultural studies. We conclude by providing suggestions for areas of research and predictions of what we should expect to discover. Overall, we highlight how important questions regarding human vocal communication across cultures can be addressed through the examination of spontaneous and volitional laughter. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.

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Section: Computational Approachesmentioning

confidence: 99%

Laughter and culture

Bryant

Bainbridge

2022

Phil. Trans. R. Soc. B

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“…Work on automatic recognition of laughter has also started to emerge but, as with the synthesis of laughter, has mostly focused the acoustic modality (e.g., [11]- [13]) and more recently on the combination of face and voice cues [14]. Less attention has been given to body laughter expressions.…”

Section: Introductionmentioning

confidence: 99%

Laughter Type Recognition from Whole Body Motion

Griffin

Aung

Romera-Paredes

et al. 2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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“…Work on automatic recognition of laughter has also started to emerge but, as with the synthesis of laughter, has mostly focused on the acoustic modality e.g., [29], [30], [31], [32], [33], [34] and more recently on the combination of face and voice cues [35], [36], [37]. Fukushima et al used electromyographic sensors to measure diaphragmatic activity, which drives laughter vocalisations, to detect laughter in people watching television [38].…”

Section: Synthesis and Recognition Of Laughtermentioning

confidence: 99%

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Griffin

Aung

Romera-Paredes

et al. 2015

IEEE Trans. Affective Comput.

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Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users' laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter "types". Second, to investigate observers' perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motioncapture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers' perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

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Discovering Emotions in Filipino Laughter Using Audio Features

Cited by 8 publications

References 8 publications

Laughter and culture

Laughter and culture

Laughter Type Recognition from Whole Body Motion

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

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