Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

By Charles B. Callaway, Khalil Sima'an. 2005

721 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/09d9e89983b07c589e35196f4a0c161987042670

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Citations in the corpus

Automating spoken dialogue management design using machine learning: An industry perspective (2008, background): “Crafting prompts for the interface of a SDS is a difficult task, and human–computer interaction researchers have shown that the exact wording of a prompt can significantly affect user performance and user perception of usability (Nass and Brave, 2005).”
Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening (2018, background): “Paralinguistic speech emphasis patterns, which give insight into the tone of voice, have been recognized to be powerful social signals (Nass and Brave 2005).”
Mirages: On Anthropomorphism in Dialogue Systems (2023, background): “Listeners may therefore attribute human characteristics, e.g. gender stereotypes, personality, and emotion, to spoken dialogue systems based on psychological and social behaviours learned through human-human communication (Nass and Brave, 2005).”
Conversational Assistants and Gender Stereotypes: Public Perceptions and Desiderata for Voice Personas (2020, background): “However, demographics and background facts are usually deliberate design choices, and research such as (Nass and Brave, 2007) shows that the gender choices of CVAs are conforming to traditional gender roles and social expectations: that is, the majority of CVAs have female personas.”
Hey Alexa ... examine the variables influencing the use of artificial intelligent in-home voice assistants (2019, background): “Importantly, individuals do not need to stop their current task to interact via voice, enabling them to multi-task (Strayer et al. 2017; Nass and Brave, 2005).”
Anthropomorphic Interactions with a Robot and Robot–like Agent (2008, background): “People stereotype software agents based on their humanlike faces (Yee, Bailenson, & Rickertsen, 2007) or voice (Nass & Brave, 2005), and they think baby–face robots are sociable (Powers & Kiesler, 2006).”
Sound as affect: Difference, power and spatiality (2016, background): “(Nass and Brave, 2005, 4) The following short soundscape composition draws attention to some of the vibrational qualities, timbres and ambiences of these technologies.”
Avatars and computer-mediated communication: a review of the definitions, uses, and effects of digital representations (2018, background): “Thus, it is unsurprising that attributions of sex and gender have remained salient in computer-mediated contexts (Biocca & Nowak, 2002; Fox, Ralston, Cooper, & Jones, 2015; Nass & Brave, 2005; Reeves & Nass, 1996; Turkle, 1995).”
How social is social responses to computers? The function of the degree of anthropomorphism in computer representations (2008, methodology): “These two humanoid robot characters and the simplistic face as used by Nowak (2004) and Nowak and Biocca (2003) all belonged to the low level of human-likeness.”
Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm (2020, methodology): “One method is to capitalize on users’ existing mental models and mimic natural forms of social interaction, creating interfaces that reflect patterns of human communication to enhance usability (Nass & Brave, 2005; Shneiderman et al., 2017).”
Real-time classification of evoked emotions using facial feature tracking and physiological responses (2008, background): “The probability that we accessed actual emotional behavior is higher than in studies that used deliberately posed faces (see Nass and Brave, 2005, for further discussion of this distinction).”
How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents (2020, background): “…components such as facial expressions (McBreen and Jack, 2001), pitch and speed of text-to-speech (Link et al., 2001), voice (Nass and Steuer, 1993; Nass and Brave, 2005), attractiveness (Khan and Sutcliffe, 2014), personality (extraversion), vivid-ness (text, voice, and animation), and…”
The science of interpersonal touch: an overview. (2010, unknown): “…emotional aspects of our other senses, such as vision and audition (and to a lesser extent olfaction and taste), than of our sense of touch (e.g., Ekman, 1993; Ekman et al., 1972; Fecteau et al., 2007; Johnstone et al., 2006; Nass and Brave, 2005; O’Doherty et al., 2001; Veldhuizen et al., 2006).”
Evaluating Anthropomorphic Product Recommendation Agents: A Social Relationship Perspective to Designing Information Systems (2009, background): “Consequently, most ttS systems sound somewhat “nonhuman” [89], which restricts the social cues they can convey [76]. as a result, we expect that Hypothesis 1c: Users will perceive greater social presence when interacting with a PRA with human speech output compared to the same PRA with TTS output.”
Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence (2021, background): “As VAs are characterized by a mode of interaction (i.e., voice), that are usually reserved to human ‐ to ‐ human exchanges (Nass & Brave, 2005), it is reasonable to expect that they are more likely to be perceived as sociable (Cho et al., 2019).”