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  4. "I'm Having Trouble Understanding You Right Now": A Multi-Dimensional Evaluation of the Intelligibility of Dysphonic Speech
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"I'm Having Trouble Understanding You Right Now": A Multi-Dimensional Evaluation of the Intelligibility of Dysphonic Speech

Full metadata

Description

Individuals with voice disorders experience challenges communicating daily. These challenges lead to a significant decrease in the quality of life for individuals with dysphonia. While voice amplification systems are often employed as a voice-assistive technology, individuals with voice disorders generally still experience difficulties being understood while using voice amplification systems. With the goal of developing systems that help improve the quality of life of individuals with dysphonia, this work outlines the landscape of voice-assistive technology, the inaccessibility of state-of-the-art voice-based technology and the need for the development of intelligibility improving voice-assistive technologies designed both with and for individuals with voice disorders. With the rise of voice-based technologies in society, in order for everyone to participate in the use of voice-based technologies individuals with voice disorders must be included in both the data that is used to train these systems and the design process. An important and necessary step towards the development of better voice assistive technology as well as more inclusive voice-based systems is the creation of a large, publicly available dataset of dysphonic speech. To this end, a web-based platform to crowdsource voice disorder speech was developed to create such a dataset. This dataset will be released so that it is freely and publicly available to stimulate research in the field of voice-assistive technologies. Future work includes building a robust intelligibility estimation model, as well as employing that model to measure, and therefore enhance, the intelligibility of a given utterance. The hope is that this model will lead to the development of voice-assistive technology using state-of-the-art machine learning models to help individuals with voice disorders be better understood.

Date Created
2020
Contributors
  • Moore, Meredith Kay (Author)
  • Panchanathan, Sethuraman (Thesis advisor)
  • Berisha, Visar (Committee member)
  • McDaniel, Troy (Committee member)
  • Venkateswara, Hemanth (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • Computer Science
  • accessibility
  • Dysphonia
  • Intelligibility
Resource Type
Text
Genre
Doctoral Dissertation
Academic theses
Extent
153 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.57181
Level of coding
minimal
Note
Doctoral Dissertation Computer Science 2020
System Created
  • 2020-06-01 08:18:37
System Modified
  • 2021-08-26 09:47:01
  •     
  • 1 year 6 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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