RESUMEN
This study uses a maze navigation task in conjunction with a quasi-scripted, prosodically controlled speech task to examine acoustic and articulatory accommodation in pairs of interacting speakers. The experiment uses a dual electromagnetic articulography set-up to collect synchronized acoustic and articulatory kinematic data from two facing speakers simultaneously. We measure the members of a dyad individually before they interact, while they are interacting in a cooperative task, and again individually after they interact. The design is ideally suited to measure speech convergence, divergence, and persistence effects during and after speaker interaction. This study specifically examines how convergence and divergence effects during a dyadic interaction may be related to prosodically salient positions, such as preceding a phrase boundary. The findings of accommodation in fine-grained prosodic measures illuminate our understanding of how the realization of linguistic phrasal structure is coordinated across interacting speakers. Our findings on individual speaker variability and the time course of accommodation provide novel evidence for accommodation at the level of cognitively specified motor control of individual articulatory gestures. Taken together, these results have implications for understanding the cognitive control of interactional behavior in spoken language communication.
Asunto(s)
Cognición/fisiología , Conducta Cooperativa , Relaciones Interpersonales , Habla/fisiología , Adulto , Fenómenos Electromagnéticos , Femenino , Humanos , Masculino , Medición de la Producción del Habla/instrumentación , Medición de la Producción del Habla/métodos , Adulto JovenRESUMEN
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.