Individual Variation in Cognitive Processing Style Predicts Differences in Phonetic Imitation of Device and Human Voices

Abstract

Phonetic imitation, or implicitly matching the acousticphonetic patterns of another speaker, has been empirically associated with natural tendencies to promote successful social communication, as well as individual differences in personality and cognitive processing style. The present study explores whether individual differences in cognitive processing style, as indexed by self-reported scored from the Autism-Spectrum Quotient (AQ) questionnaire, are linked to the way people imitate the vocal productions by two digital device voices (i.e., Apple's Siri) and two human voices. Subjects first performed a word shadowing task of human and device voices and then completed the self-administered AQ. We assessed imitation of two acoustic properties: f0 and vowel duration. We find that the attention to detail and the imagination subscale scores on the AQ mediated degree of imitation of f0 and vowel duration, respectively. The findings yield new insight to speech production and perception mechanisms and how it interacts with individual cognitive processing style differences.

Age- and Gender-Related Differences in Speech Alignment Toward Humans and Voice-AI

“…In a study of phonetic imitation between college roommates, Pardo et al (2012) found stronger alignment toward males (relative to females). The same asymmetry has been observed in studies directly comparing human and TTS voices: greater alignment toward male voices (both human and TTS) than female voices (Cohn et al, 2019;Snyder et al, 2019). Recently, a study comparing alignment toward identical TTS voices across three device platforms (varying in human-like embodiment) also showed this pattern (greater alignment toward the male voices, relative to female voices) (Cohn et al, 2020).…”

“…While Habler et al (2019) found no differences in participants' ratings of male and female TTS voices, other studies examining participants' speech behavior suggest there are some differences. For example, participants show different speech patterns toward male and female Apple Siri TTS voices, in similar directions as for real human male and female voices (Cohn et al, 2019;Snyder et al, 2019). This suggests that more subconscious behavior may reveal gender-mediated patterns (if present) in human-device interactions.…”

“…Target words consisted of 12 monosyllabic English low-frequency words: bomb, sewn, vine, pun, shun, chime, yawn, shone, wane, tame, wren, hem (mean log frequency: 1.6; range: 1.1-2.1 (Brysbaert and New, 2009)) used in related imitation experiments with human and device interlocutors (Cohn et al, 2019;Snyder et al, 2019;Cohn et al, 2020). Stimuli consisted of recordings of the words produced by four distinct voices: two human and two AI device voices.…”

“…This suggests that more subconscious behavior may reveal gender-mediated patterns (if present) in human-device interactions. Yet, in all three of these studies (Cohn et al, 2019;Habler et al, 2019;Snyder et al, 2019), only a young adult population (e.g., college-age students) was examined. How might gender-mediated patterns emerge across different age groups?…”

“…Socially-mediated imitation patterns are often interpreted through the lens of Communication Accommodation Theory (CAT) (Giles et al, 1991;Shepard, 2001), which proposes that speakers use linguistic alignment to emphasize or minimize social differences between themselves and their interlocutors. The CAT framework can also be applied to understand humandevice interaction: recent studies that make a direct comparison between human and voice-AI interlocutors found greater vocal imitation for the human, relative to the voice-AI speaker (e.g., Apple's Siri in Cohn et al, 2019;Snyder et al, 2019;Amazon's Alexa in Raveh et al, 2019;Zellou and Cohn, 2020). Less speech alignment toward digital device assistants suggests that people may be less inclined to demonstrate social closeness toward voice-AI, as they do for humans.…”