User Experience with Smart Voice Assistants: The Accent Perspective

Pal, Debajyoti; Arpnikanondt, Chonlameth; Funilkul, Suree; Varadarajan, Vijayakumar

doi:10.1109/icccnt45670.2019.8944754

Cited by 40 publications

(23 citation statements)

References 27 publications

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“…In this respect, the choice of appropriate vocabulary is important that can be understood by a variety of users. Previous work in [37] also indicated problems with usability with respect to the accent of English spoken. Since, the voice-assistants lack any type of a visual interface, it can lead to a higher cognitive load among the users as they must remember all the speech commands.…”

Section: The Vus Scalementioning

confidence: 97%

“…Therefore, how good or bad SUS may represent the usability scenario of the voice-assistants cannot be answered. Authors in [37] test Siri and Alexa with respect to their ability to understand commands from native and non-native English language speakers for performing certain common tasks. Results suggest no significant differences in usability between the two user groups.…”

Section: Current State-of-art Of Voice-assistantsmentioning

confidence: 99%

“…Voice-assistants have come into prominence very recently and they have a number of unique features that are not addressed by usability evaluation scales targeted toward GUI-based systems. For example, speech recognition is an important aspect of voice-assistants, and despite improvements in speech recognition technology, they make errors [36,37]. Consequently, they must give feedback to the users as to what has been recognized.…”

Section: Sn Computer Sciencementioning

confidence: 99%

See 2 more Smart Citations

Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

2021

Self Cite

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Section: The Vus Scalementioning

confidence: 97%

Section: Current State-of-art Of Voice-assistantsmentioning

confidence: 99%

Section: Sn Computer Sciencementioning

confidence: 99%

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Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

2021

Self Cite

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“…Continued use [33]; Satisfaction [33]; System efectiveness [24]; Tone clarity [8]; Satisfaction [3]; System Usability Scale (SUS) [6,44]; Speech User Interface Service Quality Questionnaire (SUISQ-R) [6]; Document feedback [38]; Voice understandability [10]; Perception of rapport [46]; Group decision performance [46]; Voice performance [11] Social presence [32,33]; Intimacy [25,33]; Closeness (Subjective closeness index) [53];…”

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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“…In turn, an incorrect voice search may decrease the user-perceived quality of the whole system. Moreover, speech assistants’ low personalization ability generally leads to deterioration of user experience [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

Laptev

Andrusenko

Podluzhny

et al. 2021

Sensors

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With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. For on-device speech recognition tasks, researchers and industry prefer end-to-end ASR systems as they can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Personalization, which is mainly handling out-of-vocabulary (OOV) words, is another challenging task associated with speech assistants. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. We propose a method of dynamic acoustic unit augmentation based on the Byte Pair Encoding with dropout (BPE-dropout) technique. The method non-deterministically tokenizes utterances to extend the token’s contexts and to regularize their distribution for the model’s recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative word error rate (WER) and 25% relative F-score) at no additional computational cost. Owing to the BPE-dropout use, our monolingual Turkish Conformer has achieved a competitive result with 22.2% character error rate (CER) and 38.9% WER, which is close to the best published multilingual system.

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User Experience with Smart Voice Assistants: The Accent Perspective

Cited by 40 publications

References 27 publications

Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

Measuring Voice UX Quantitatively

Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

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