Quantitative analysis of multimodal speech data

Abstract

This 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.