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

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.

Prosodic Differences in Human- and Alexa-Directed Speech, but Similar Local Intelligibility Adjustments

“…Additionally, the present study used two types of voices; it is possible that other paralinguistic features of those voices might have mediated speech style adjustments. For example, recent work has shown that speakers align speech differently toward TTS voices that "sound" older (e.g., Apple's Siri voices, rated in their 40 and 50s) (Zellou et al, 2021). Furthermore, there is work showing that introducing "charismatic" features from human speakers' voices shapes perception of TTS voices (Fischer et al, 2019;Niebuhr and Michalsky, 2019).…”

“…First, the interlocutor introduced themselves and then went through voice-over instructions with the participant. Participants saw an image corresponding to the interlocutor category: stock images of "adult female" (used in prior work; Zellou et al, 2021) and "Amazon Alexa" (2nd Generation Black Echo).…”

“…A growing body of research has begun to investigate the social, cognitive, and linguistic effects of humans interacting with voice-AI (Purington et al, 2017;Arnold et al, 2019;Cohn et al, 2019b;Burbach et al, 2019). For example, recent work has shown that listeners attribute human-like characteristics to the text-tospeech (TTS) output used for modern voice-AI, including personality traits (Lopatovska, 2020), apparent age (Cohn et al, 2020a;Zellou et al, 2021), and gender (Habler et al, 2019;Loideain and Adams, 2020). While the spread of voice-AI assistants is undeniable-particularly in the United States-there are many open scientific questions as to the nature of people's interactions with voice-AI.…”

“…For example, people appear to apply politeness norms from humanhuman interaction to computers: giving more favorable ratings when a computer directly asks about its own performance, relative to when a different computer elicits this information (Nass et al, 1994;Hoffmann et al, 2009). In line with technology equivalence accounts, there is some evidence for applied social behaviors to voice-AI in the way people adjust their speech, such as gendermediated vocal alignment (Cohn et al, 2019b;Zellou et al, 2021). In the present study, one prediction from technology equivalence accounts is that people will adjust their speech patterns when talking to voice-AI and humans in similar ways if the communicative context is controlled.…”

Sound change in spontaneous bilingual speech: A corpus study on the Cantonese n-l merger in Cantonese-English bilinguals

“…Conversely, a recent study of the same phenomenon in spontaneous speech found that early Cantonese-English bilinguals were less likely to release final stops in English than non-Cantonese-English bilinguals [20]. These conflicting outcomes simply illustrate the need to examine variation in speech across styles and registers, as this variation has maximum utility for ASR systems and the development of NLP tools for speech and language, given how little is know about how talkers interact with such systems [21].…”