Abstract
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.
Highlights
Speech is a common mode for interfacing with technology; people of all ages regularly talk to voice-activated artificially intelligent devices, such as Apple’s Siri and Amazon’s Alexa (Bentley et al, 2018)
Shadowers aligned to the model talkers, in rates comparable to those reported in prior studies (e.g., Pardo et al, 2017)
There was an interaction between Shadower Gender and Model Talker Humanness, which is plotted in Figure 2: male shadowers align more to human voices overall
Summary
Speech is a common mode for interfacing with technology; people of all ages regularly talk to voice-activated artificially intelligent (voice-AI) devices, such as Apple’s Siri and Amazon’s Alexa (Bentley et al, 2018). Millions of voice-AI devices are being used in people’s homes (Ammari et al, 2019) and almost half of Americans report using a digital assistant (Olmstead, 2017). In some ways, these systems exhibit more human-like qualities: conveyed by their names (e.g., “Alexa”), speech patterns, and personas. Many voice-AI systems display an apparent gender. Gender is an indexical variable in human speech that has been found to shape the communicative behavior of interacting speakers (Eckert and McConnell-Ginet, 1992). How do people respond to apparent gender in a voice-AI system?
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