Cracking the social code of speech prosody using reverse correlation

Abstract : Human listeners excel at forming high-level social representations about each other, even from the briefest of utterances. In particular , pitch is widely recognized as the auditory dimension that conveys most of the information about a speaker's traits, emotional states, and attitudes. While past research has primarily looked at the influence of mean pitch, almost nothing is known about how intonation patterns, i.e., finely tuned pitch trajectories around the mean, may determine social judgments in speech. Here, we introduce an experimental paradigm that combines state-of-the-art voice transformation algorithms with psychophysical reverse correlation and show that two of the most important dimensions of social judgments, a speaker's perceived dominance and trust-worthiness, are driven by robust and distinguishing pitch trajec-tories in short utterances like the word "Hello," which remained remarkably stable whether male or female listeners judged male or female speakers. These findings reveal a unique communicative adaptation that enables listeners to infer social traits regardless of speakers' physical characteristics, such as sex and mean pitch. By characterizing how any given individual's mental representations may differ from this generic code, the method introduced here opens avenues to explore dysprosody and social-cognitive deficits in disorders like autism spectrum and schizophrenia. In addition, once derived experimentally, these prototypes can be applied to novel utterances, thus providing a principled way to modulate personality impressions in arbitrary speech signals. speech | voice | prosody | social traits | reverse-correlation
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Emmanuel Ponsot, Juan Burred, Pascal Belin, Jean-Julien Aucouturier. Cracking the social code of speech prosody using reverse correlation. Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2018, 115 (15), pp.3972-3977. ⟨10.1073/pnas.1716090115⟩. ⟨hal-02004519⟩

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