Quantitative analysis of multimodal speech data

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dc.creatorSamantha Gordon Danner
dc.creatorAdriano Vilela Barbosa
dc.creatorLouis Goldstein
dc.date.accessioned2025-04-22T15:00:04Z
dc.date.accessioned2025-09-08T23:15:10Z
dc.date.available2025-04-22T15:00:04Z
dc.date.issued2018
dc.identifier.doihttps://doi.org/10.1016/j.wocn.2018.09.007
dc.identifier.issn00954470
dc.identifier.urihttps://hdl.handle.net/1843/81740
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofJournal of Phonetics
dc.rightsAcesso Restrito
dc.subjectProcesso estocástico
dc.subject.otherMultimodal speech
dc.subject.otherBodily gesture
dc.subject.otherFlowAnalyzer
dc.subject.othercorrelation map analysis
dc.subject.otherTime-varying coordination
dc.subject.otherCommunicative context
dc.titleQuantitative analysis of multimodal speech data
dc.typeArtigo de periódico
local.citation.epage283
local.citation.spage268
local.citation.volume71
local.description.resumoThis study presents techniques for quantitatively analyzing coordination and kinematics in multimodal speech using video, audio and electromagnetic articulography (EMA) data. Multimodal speech research has flourished due to recent improvements in technology, yet gesture detection/annotation strategies vary widely, leading to difficulty in generalizing across studies and in advancing this field of research. We describe how FlowAnalyzer software can be used to extract kinematic signals from basic video recordings; and we apply a technique, derived from speech kinematic research, to detect bodily gestures in these kinematic signals. We investigate whether kinematic characteristics of multimodal speech differ dependent on communicative context, and we find that these contexts can be distinguished quantitatively, suggesting a way to improve and standardize existing gesture identification/annotation strategy. We also discuss a method, Correlation Map Analysis (CMA), for quantifying the relationship between speech and bodily gesture kinematics over time. We describe potential applications of CMA to multimodal speech research, such as describing characteristics of speech-gesture coordination in different communicative contexts. The use of the techniques presented here can improve and advance multimodal speech and gesture research by applying quantitative methods in the detection and description of multimodal speech.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0095447017302280

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