Effects of Smart Virtual Assistants' Gender and Language

Habler, Florian; Schwind, Valentin; Henze, Niels

doi:10.1145/3340764.3344441

Cited by 26 publications

(10 citation statements)

References 12 publications

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“…Only a handful of studies have tested whether speakers display different gender-mediated behaviors toward voice-AI, such as for (apparent) male and female text-to-speech (TTS) voices. 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).…”

Section: Introductionmentioning

confidence: 58%

“…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?…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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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Section: Introductionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…The Computers are Social Actors (CASA; Nass et al, 1997;Nass et al, 1994) framework proposes that people apply socially mediated, 'rules', from human-human interaction to computers when they detect a cue of 'humanity' in the system. Voice-AI systems are already imbued with multiple human-like features: they have names, apparent genders Habler et al (2019) and interact with users using spoken language. Indeed, there is some evidence that individuals engage with voice-AI in ways that parallel the ways they engage with humans (e.g., gender-asymmetries in phonetic alignment in Cohn et al, 2019;Zellou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Speech Rate Adjustments in Conversations With an Amazon Alexa Socialbot

et al. 2021

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This paper investigates users’ speech rate adjustments during conversations with an Amazon Alexa socialbot in response to situational (in-lab vs. at-home) and communicative (ASR comprehension errors) factors. We collected user interaction studies and measured speech rate at each turn in the conversation and in baseline productions (collected prior to the interaction). Overall, we find that users slow their speech rate when talking to the bot, relative to their pre-interaction productions, consistent with hyperarticulation. Speakers use an even slower speech rate in the in-lab setting (relative to at-home). We also see evidence for turn-level entrainment: the user follows the directionality of Alexa’s changes in rate in the immediately preceding turn. Yet, we do not see differences in hyperarticulation or entrainment in response to ASR errors, or on the basis of user ratings of the interaction. Overall, this work has implications for human-computer interaction and theories of linguistic adaptation and entrainment.

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“…The consistency of the voice here limited the impact of the gender that is usually seen. Habler et al [56] also found that the speech content has much more influence than the gender. Indeed, if female voice were slightly favored, low-status language was significantly preferred whatever the genre.…”

Section: Acousticsmentioning

confidence: 97%

A Review of Virtual Assistants’ Characteristics: Recommendations for Designing an Optimal Human–Machine Cooperation

Gilles

Bevacqua

2022

Journal of Computing and Information Science in Engineering

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Designed to improve human-machine interactions, virtual agents, and particularly virtual assistants (VAs), are spreading in our daily lives. Presenting a very wide variety of characteristics, studies generally report their own agent with its own characteristics and objective. So we can wonder if some of these characteristics are a consensus for VAs in general. Within this work, we aim to identify the agents' characteristics that should be considered when designing a virtual assistant promoting the best communication and cooperation between man and machine. We review the aspects of representation of the agent (embodied or not) and its ability to interact with the human being whether by speech or gestures, but also by displaying personality traits. This overview makes some focuses on virtual assistance of any kind embarked on vehicles.

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Effects of Smart Virtual Assistants' Gender and Language

Cited by 26 publications

References 12 publications

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

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

Speech Rate Adjustments in Conversations With an Amazon Alexa Socialbot

A Review of Virtual Assistants’ Characteristics: Recommendations for Designing an Optimal Human–Machine Cooperation

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