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 untapped 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 delivery 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 in genuine fact-finding efforts. Our easy-to-use R package, speech, implements the model and many more tools for audio analysis.
Highlights
Speech and dialogue are at the heart of politics
We introduce a new method that allows researchers to measure vocal tone in high-dimensional audio data and study how it is used in political interactions
In “A Model of Conversation Dynamics,” we address this by developing a semisupervised model for human speech, which infers latent tones of voice and provides a principled measure of uncertainty for the estimated patterns in their use
Summary
Speech and dialogue are the heart of politics: nearly every political institution in the world involves verbal communication. 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. Our easy-to-use R package, communication, implements the model and many more tools for audio analysis.
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