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Quantitative Analysis of Multimodal Speech Data.

Samantha Gordon Danner1, Adriano Vilela Barbosa2, Louis Goldstein1

  • 1University of Southern California, Los Angeles, CA, 90089, USA.

Journal of Phonetics
|January 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces quantitative methods for analyzing multimodal speech and gestures using video and audio data. These techniques can standardize gesture analysis and improve the understanding of speech-gesture coordination in various contexts.

Keywords:
Correlation Map AnalysisFlowAnalyzerMultimodal speechbodily gesturecommunicative contexttime-varying coordination

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Area of Science:

  • Speech and communication sciences
  • Human-computer interaction
  • Biomedical engineering

Background:

  • Multimodal speech research is advancing due to technological improvements.
  • Current gesture detection and annotation strategies lack standardization, hindering research generalization.
  • Quantitative analysis methods are needed to advance multimodal speech research.

Purpose of the Study:

  • To present quantitative techniques for analyzing multimodal speech kinematics.
  • To investigate context-dependent kinematic differences in multimodal speech.
  • To introduce a method for quantifying speech-gesture coordination.

Main Methods:

  • Utilizing FlowAnalyzer software to extract kinematic signals from video recordings.
  • Applying speech kinematic research techniques for bodily gesture detection.
  • Employing Correlation Map Analysis (CMA) to quantify speech-gesture kinematics over time.

Main Results:

  • Communicative contexts can be quantitatively distinguished based on kinematic characteristics.
  • The study provides a method to improve and standardize gesture identification and annotation.
  • Correlation Map Analysis (CMA) offers a way to describe speech-gesture coordination.

Conclusions:

  • The presented quantitative techniques enhance the detection and description of multimodal speech.
  • Standardized analysis methods will advance multimodal speech and gesture research.
  • Improved understanding of speech-gesture coordination across different contexts is achievable.