Speech alignment is where talkers subconsciously adopt the speech and language patterns of their interlocutor. Nowadays, people of all ages are speaking with voice-activated, artificially-intelligent (voice-AI) digital assistants through phones or smart speakers. This study examines participants’ age (older adults, 53–81 years old vs. younger adults, 18–39 years old) and gender (female and male) on degree of speech alignment during shadowing of (female and male) human and voice-AI (Apple’s Siri) productions. Degree of alignment was assessed holistically via a perceptual ratings AXB task by a separate group of listeners. Results reveal that older and younger adults display distinct patterns of alignment based on humanness and gender of the human model talkers: older adults displayed greater alignment toward the female human and device voices, while younger adults aligned to a greater extent toward the male human voice. Additionally, there were other gender-mediated differences observed, all of which interacted with model talker category (voice-AI vs. human) or shadower age category (OA vs. YA). Taken together, these results suggest a complex interplay of social dynamics in alignment, which can inform models of speech production both in human-human and human-device interaction.

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Listeners show better-than-chance discrimination of nasalized and oral vowels occurring in appropriate consonantal contexts. Yet, the methods for investigating partial perceptual compensation for nasal coarticulation often include nasal and oral vowels containing naturally different pitch contours. Listeners may therefore be discriminating between these vowels based on pitch differences and not nasalization. The current study investigates the effect of pitch variation on the discrimination of nasalized and oral vowels in C_N and C_C items. The f0 contour of vowels within paired discrimination trials was varied. The results indicate that pitch variation does not influence patterns of partial perceptual compensation for coarticulation.

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