Voicesetting

Fiannaca, Alexander J.; Paradiso, Ann; Campbell, Jon; Morris, Meredith Ringel

doi:10.1145/3173574.3173857

Cited by 17 publications

(4 citation statements)

References 17 publications

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“…TTS systems have been widely deployed in voice assistants such as Cortana, Alexa, Siri, and the Google Assistant; TTS is also key to many assistive technologies, including screen readers used by people who are blind and AAC devices used by people with speech and motor disabilities. Voice banking to create personalized voice fonts may be particularly valued by people with degenerative conditions that result in progressive loss of speaking abilities (e.g., ALS) [19,38].…”

Section: Speech Generationmentioning

confidence: 99%

Toward fairness in AI for people with disabilities SBG@a research roadmap

Guo

Kamar

Vaughan

et al. 2020

SIGACCESS Access. Comput.

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AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.

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Section: Speech Generationmentioning

confidence: 99%

Toward fairness in AI for people with disabilities SBG@a research roadmap

Guo

Kamar

Vaughan

et al. 2020

SIGACCESS Access. Comput.

Self Cite

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“…Text entry [9,38,67,71,82,85,93] Application control [22,90,92] Speech analysis [26,36,52,68,72] Spoken output [28,31,66,77] Conversational Agents [33,43,45,48,55,61,96] Probes [18,74] Domains Education [22,43,52,61,68,77] Health & Well-being [26,28,36,71,72,92,94] Transportation [33,38,55,67,90] Gaming [18,26,36,52,66,…”

Section: References Themesmentioning

confidence: 99%

“…Moran et al [66] integrated text-to-speech with a pervasive mobile game called 'Cargo' to provide instructions to multiple teams of players. Fiannaca et al [31] looked to provide expressivity to the speech that generated from text-to-speech engines through a touch screen interface. The model allows expressivity through the insertion of emoji, punctuation, vocal sound effects like laughter into the synthesised speech, which was highly valued by end-users.…”

Section: Spoken Outputmentioning

confidence: 99%

“…These games are interesting in that they, primarily, take advantage of the stock text-to-speech modules included on commercial CUIs resulting in "a monotone Alexa saying things like, 'Oh, my, I'm really scared now,' in the exact same tone she replies that she's turned your lights off" [12]. Providing tools to allow developers and designers to imbue emotion in the generated speech, taking advantage of the research done for assistive technologies such as [31] for example, would allow more expressive and immersive games without the expense of voice actors. This would also give the possibility to procedurally generate the game or story dialogue.…”

Section: Gamesmentioning

confidence: 99%

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Conversational User Interfaces on Mobile Devices

Jaber

2020

Proceedings of the 2nd Conference on Conversational User Interfaces

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Conversational User Interfaces (CUI) on mobile devices are the most accessible and widespread examples of voice-based interaction in the wild. This paper presents a survey of mobile conversation user interface research since the commercial deployment of Apple's Siri, the first readily available consumer CUI. We present and discuss Text Entry & Typing, Application Control, Speech Analysis, Conversational Agents, Spoken Output, & Probes as the prevalent themes of research in this area. We also discuss this body of work in relation to the domains of Health & Well-being, Education, Games, and Transportation. We conclude this paper with a discussion on Multi-modal CUIs, Conversational Repair, and the implications for CUIs of greater access to the context of use. CCS CONCEPTS• Computing methodologies → Speech recognition; • Humancentered computing → Interaction techniques; Smartphones; • General and reference → Surveys and overviews.

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Synthesising Expressive Speech – Which Synthesiser for VOCAs?

Wülfing

Dang

André

2020

Text, Speech, and Dialogue

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In the context of people with complex communication needs who depend on Voice Output Communication Aids, the ability of speech synthesisers to convey not only sentences, but also emotions would be a great enrichment. The latter is essential and very natural in interpersonal speech communication. Hence, we are interested in the expressiveness of speech synthesisers and their perception. We present the results of a study in which 82 participants listened to different synthesised sentences with different emotional contours from three synthesisers. We found that participants' ratings on expressiveness and naturalness indicate that the synthesiser CereVoice performs better than the other synthesisers.

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Voicesetting

Cited by 17 publications

References 17 publications

Toward fairness in AI for people with disabilities SBG@a research roadmap

Toward fairness in AI for people with disabilities SBG@a research roadmap

Conversational User Interfaces on Mobile Devices

Synthesising Expressive Speech – Which Synthesiser for VOCAs?

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