A Dynamic Model of Speech for the Social Sciences

Abstract

Speech and dialogue are the heart of politics: nearly every political institution in the world involves verbal communication. Yet vast literatures on political communication focus almost exclusively on what words were spoken, entirely ignoring how they were delivered - auditory cues that convey emotion, signal positions, and establish reputation. We develop a model that opens this information to principled statistical inquiry: the model of audio and speech structure (MASS). Our approach models political speech as a stochastic process shaped by fixed and time-varying covariates, including the history of the conversation itself. In an application to Supreme Court oral arguments, we demonstrate how vocal tone signals crucial information - skepticism of legal arguments - that is indecipherable to text models. Results show that justices do not use questioning to strategically manipulate their peers but rather engage sincerely with the presented arguments. Our easy-to-use R package, communication, implements the model and many more tools for audio analysis.