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Evolution of human computer interaction
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In: Sci. Visualization ; Scientific Visualization (2021)
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Using Spoken Dialogue Technology for L2 Speaking Practice: What Do Teachers Think?
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In: Applied Linguistics Faculty Publications and Presentations (2020)
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Fourteen-channel EEG with Imagined Speech (FEIS) dataset ...
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Fourteen-channel EEG with Imagined Speech (FEIS) dataset ...
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Usability of Automatic Speech Recognition Systems for Individuals with Speech Disorders: Past, Present, Future, and A Proposed Model
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Abstract:
People are using voice assistants (VAs) such as Siri & Alexa more than ever before. With 46% of U.S. adults using VAs, commercially available voice-activated technologies are becoming pervasive in our homes and beyond (Pew Research, 2017). VAs provide convenience, novelty, and unique solutions for the medical industry. But, some users may be left out of the conversation. People with speech disorders or atypical speech historically have found difficulty with using automatic speech recognition (ASR) technologies, the precursor to VAs. Usability testing for these systems has consistently shown that they are not easy to use for people with speech disorders. This investigation sought to perform a literature review of the existing research on the usability of commercially available ASRs for people with speech disorders to provide historical perspectives and to take an inventory of how this issue is being addressed today. A literature review was performed on the usability of commercially available ASRs for people with speech disorders and was divided into two stages: studies before the introduction of VAs and those that tested VAs themselves. Understanding where we have been and where we are now will also inform technical communication and usability professionals on what the future of ASRs may hold and how we can best address the needs of this audience. To do so, this paper proposes solutions for inclusive design in the voice assistant design space including a conceptual model for integrating specific techniques into commercially available VAs.
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Keyword:
automatic speech recognition; dysarthria; human factors; human-computer interaction; inclusive design; speech disorders; usability; user experience; voice assistant; voice interfaces
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URL: http://hdl.handle.net/11299/202757
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New Directions in Treatments Targeting Stroke Recovery.
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In: Stroke, vol 49, iss 12 (2018)
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EXPLORING THE ACCESSIBILITY OF HOME-BASED, VOICE-CONTROLLED INTELLIGENT PERSONAL ASSISTANTS ...
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Pradhan, Alisha. - : Digital Repository at the University of Maryland, 2018
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Geração de prosódia para o português brasileiro em sistemas text-to-speech ; Prosody generation for Brazilian Portuguese in text-to-speech systems
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Sá, Felipe Cortez de. - : Universidade Federal do Rio Grande do Norte, 2018. : Brasil, 2018. : UFRN, 2018. : Ciência da Computação, 2018
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Speech Emotion Recognition using Convolutional Neural Networks
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In: Computer Science and Engineering: Theses, Dissertations, and Student Research (2018)
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EXPLORING THE ACCESSIBILITY OF HOME-BASED, VOICE-CONTROLLED INTELLIGENT PERSONAL ASSISTANTS
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ENHANCING EXPRESSIVITY OF DOCUMENT-CENTERED COLLABORATION WITH MULTIMODAL ANNOTATIONS
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Paralinguistic Speech Recognition: Classifying Emotion in Speech with Deep Learning Neural Networks
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In: Senior Projects Spring 2016 (2016)
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Empirical evidence for a diminished sense of agency in speech interfaces
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ДВУЯЗЫЧНАЯ МНОГОМОДАЛЬНАЯ СИСТЕМА ДЛЯ АУДИОВИЗУАЛЬНОГО СИНТЕЗА РЕЧИ И ЖЕСТОВОГО ЯЗЫКА ПО ТЕКСТУ
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КАРПОВ АЛЕКСЕЙ АНАТОЛЬЕВИЧ; ЖЕЛЕЗНЫ МИЛОШ. - : Федеральное государственное автономное образовательное учреждение высшего образования «Санкт-Петербургский национальный исследовательский университет информационных технологий, механики и оптики», 2014
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The additive effect of turn-taking cues in human and synthetic voice
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: Elsevier, 2012
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Modeling Coarticulation in EMG-based Continuous Speech Recognition
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In: Speech Communication, 52 (4), 341-353 ; ISSN: 0167-6393 (2012)
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Impact of different speech interfaces of personal devices on users' perception
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Modeling Coarticulation in EMG-based Continuous Speech Recognition
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: Elsevier, 2011
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The additive effect of turn-taking cues in human and synthetic voice
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-00699045 ; Speech Communication, Elsevier : North-Holland, 2010, 53 (1), pp.23. ⟨10.1016/j.specom.2010.08.003⟩ (2010)
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Small-vocabulary speech recognition for resource-scarce languages
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In: http://www.cs.cmu.edu/~roni/papers/sigdev2010-final7.pdf (2010)
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