Mental Workload and Language Production in Non-Native Speaker IPA Interaction

Wu, Yunhan; Edwards, Justin D.; Cooney, Orla; Bleakley, Anna; Doyle, Philip; Clark, Leigh; Rough, Daniel; Cowan, Benjamin R.

doi:10.1145/3405755.3406118

Cited by 19 publications

(12 citation statements)

References 34 publications

(54 reference statements)

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“…The cognitive demand of interaction is particularly acute for non-native (L2) speakers. In a recent study, users who had to use their non-native language to interact with a voice agent experienced significantly higher levels of cognitive load than those who could interact with their native language [Wu et al 2020a]. For these users, pairing speech with visual feedback may be critical in supporting the interaction [Wu et al 2020b].…”

Section: The Drawbacks Of Using Speechmentioning

confidence: 99%

Building and Designing Expressive Speech Synthesis

Aylett¹,

Clark²,

Cowan³

et al. 2021

The Handbook on Socially Interactive Agents

Self Cite

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You all know the test for artificial intelligence -the Turing test. A human judge has a conversation with a human and a computer. If the judge can't tell the machine apart from the human, the machine has passed the test. I now propose a test for computer voices -the Ebert test. If a computer voice can successfully tell a joke and do the timing and delivery as well as Henny Youngman, then that's the voice I want." -Roger Ebert.

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Section: The Drawbacks Of Using Speechmentioning

confidence: 99%

Building and Designing Expressive Speech Synthesis

Aylett¹,

Clark²,

Cowan³

et al. 2021

The Handbook on Socially Interactive Agents

Self Cite

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“…It applies to "bodyless" and ever-present voice systems, where there is not necessarily a visual or physical interface to begin or conduct interactions. The table also [39]; Exercise Behavior [37]; Gaze [16]; Language production (e.g., lexical complexity, adaptation) [51]; Interaction time [27]; Driving performance [38]; Disclosure (user responses) [53]; Accidents [41] Subj…”

Section: Ivs and IV Frameworkmentioning

confidence: 99%

Measuring Voice UX Quantitatively

Seaborn

Urakami

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Computer voice is experiencing a renaissance through the growing popularity of voice-based interfaces, agents, and environments. Yet, how to measure the user experience (UX) of voice-based systems remains an open and urgent question, especially given that their form factors and interaction styles tend to be non-visual, intangible, and often considered disembodied or "body-less. " As a frst step, we surveyed the ACM and IEEE literatures to determine which quantitative measures and measurements have been deemed important for voice UX. Our fndings show that there is little consensus, even with similar situations and systems, as well as an overreliance on lab work and unvalidated scales. In response, we ofer two high-level descriptive frameworks for guiding future research, developing standardized instruments, and informing ongoing review work. Our work highlights the current strengths and weaknesses of voice UX research and charts a path towards measuring voice UX in a more comprehensive way. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Human computer interaction (HCI); Interaction devices; Sound-based input / output.

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“…We should nevertheless consider that there is a risk that humans will interact with robots that utter fragmented instructions differently than how they do with human instructors. There is evidence that users tend to vary their behaviour when they interact with computers, in comparison to how they interact with humans (Wu et al, 2020 ). It is therefore likely users would adapt to systems that provide too little or too much information, even when clearly violating the maxims of quantity (Grice, 1975 ), and when such behaviour from humans could appear to be odd.…”

Section: Implications For the Design Of Conversational Interfacesmentioning

confidence: 99%

Measuring Collaboration Load With Pupillary Responses - Implications for the Design of Instructions in Task-Oriented HRI

Kontogiorgos

Gustafson

2021

Front. Psychol.

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In face-to-face interaction, speakers establish common ground incrementally, the mutual belief of understanding. Instead of constructing “one-shot” complete utterances, speakers tend to package pieces of information in smaller fragments (what Clark calls “installments”). The aim of this paper was to investigate how speakers' fragmented construction of utterances affect the cognitive load of the conversational partners during utterance production and comprehension. In a collaborative furniture assembly, participants instructed each other how to build an IKEA stool. Pupil diameter was measured as an outcome of effort and cognitive processing in the collaborative task. Pupillometry data and eye-gaze behaviour indicated that more cognitive resources were required by speakers to construct fragmented rather than non-fragmented utterances. Such construction of utterances by audience design was associated with higher cognitive load for speakers. We also found that listeners' cognitive resources were decreased in each new speaker utterance, suggesting that speakers' efforts in the fragmented construction of utterances were successful to resolve ambiguities. The results indicated that speaking in fragments is beneficial for minimising collaboration load, however, adapting to listeners is a demanding task. We discuss implications for future empirical research on the design of task-oriented human-robot interactions, and how assistive social robots may benefit from the production of fragmented instructions.

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Mental Workload and Language Production in Non-Native Speaker IPA Interaction

Cited by 19 publications

References 34 publications

Building and Designing Expressive Speech Synthesis

Building and Designing Expressive Speech Synthesis

Measuring Voice UX Quantitatively

Measuring Collaboration Load With Pupillary Responses - Implications for the Design of Instructions in Task-Oriented HRI

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