Capturing, Representing, and Interacting with Laughter

Ryokai, Kimiko; López, Elena Durán; Howell, Noura; Gillick, Jon; Bamman, David

doi:10.1145/3173574.3173932

Cited by 32 publications

(18 citation statements)

References 32 publications

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“…We obtain videos from seasons 1-8 and segment episodes using laughter cues from its audience. Specifically, we use open-source software for laughter detection (Ryokai et al, 2018) to obtain initial segmentation boundaries and fine-tune them using the subtitles' timestamps.…”

Section: Data Collectionmentioning

confidence: 99%

Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

Castro¹,

Hazarika²,

Pérez-Rosas³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

138

107

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Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic classification of sarcasm. As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUS-tARD 1 ), compiled from popular TV shows. MUStARD consists of audiovisual utterances annotated with sarcasm labels. Each utterance is accompanied by its context of historical utterances in the dialogue, which provides additional information on the scenario where the utterance occurs. Our initial results show that the use of multimodal information can reduce the relative error rate of sarcasm detection by up to 12.9% in F-score when compared to the use of individual modalities. The full dataset is publicly available for use at https://github. com/soujanyaporia/MUStARD.

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Section: Data Collectionmentioning

confidence: 99%

Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

Castro¹,

Hazarika²,

Pérez-Rosas³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

138

107

View full text Add to dashboard Cite

show abstract

“…Subsequently, interaction chronograms (including turn-taking events) were created using TextGridTools, a Python toolkit for working with Praat TextGrid files (Buschmeier and Włodarczak, 2013 ). Intervals of laughter were identified using the method by Ryokai et al ( 2018 ) with the code and models accompanying the paper 3 . The authors report a per-frame accuracy of 88% on a held-out Switchboard test set, which is comparable to state-of-the-art performance of audio-only automatic laughter recognizers (Cosentino et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Breathing in Conversation

Włodarczak

Heldner

2020

Front. Psychol.

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This work revisits the problem of breathing cues used for management of speaking turns in multiparty casual conversation. We propose a new categorization of turn-taking events which combines the criterion of speaker change with whether the original speaker inhales before producing the next talkspurt. We demonstrate that the latter criterion could be potentially used as a good proxy for pragmatic completeness of the previous utterance (and, by extension, of the interruptive character of the incoming speech). We also present evidence that breath holds are used in reaction to incoming talk rather than as a turn-holding cue. In addition to analysing dimensions which are routinely omitted in studies of interactional functions of breathing (exhalations, presence of overlapping speech, breath holds), the present study also looks at patterns of breath holds in silent breathing and shows that breath holds are sometimes produced toward the beginning (and toward the top) of silent exhalations, potentially indicating an abandoned intention to take the turn. We claim that the breathing signal can thus be successfully used for uncovering hidden turn-taking events, which are otherwise obscured by silence-based representations of interaction.

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“…Ryolai et al [ 168 ] explored ways to assign laughter to tangible objects. Yuan et al [ 169 ] trained a deep learning model to predict the scannability of webpage content.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Capturing, Representing, and Interacting with Laughter

Cited by 32 publications

References 32 publications

Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

Breathing in Conversation

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

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