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• Self-supervised learning using unlabeled speech with multiple types of speech disorder for disordered speech recognition

  Ryoichi Takashima, Takeru Otani, Ryo Aihara, Tetsuya Takiguchi, Shinya Taguchi · 2024 · Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2024)

  This paper tackles a critical barrier in accessible speech technology: automatic speech recognition (ASR) systems perform poorly for people with speech disorders because they are trained almost exclusively on typical speech. The authors from Kobe University and Mitsubishi…

  speech recognition · speech disorders · machine learning · self-supervised learning · assistive technology

• WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users

  Yunzhi Li, Vimal Mollyn, Kuang Yuan, Patrick Carrington · 2024 · ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents WheelPoser, a real-time body pose estimation system designed specifically for wheelchair users using only four inertial measurement units (IMUs). Existing pose estimation methods are largely developed for standing and ambulatory users, relying on assumptions…

  wheelchair users · pose estimation · inertial measurement units · motion capture · body tracking

• Vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study

  Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do, Nicholas Price, Arthur Lowery, Kim Marriott · 2024 · ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper reveals a significant research gap in assistive technology for people with cerebral visual impairment (CVI) through a scoping review and focus studies. CVI is caused by damage to the brain's visual processing centres rather than the eyes themselves, making it…

  cerebral visual impairment · CVI · vision-based assistive technology · computer vision · augmented reality

• Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges

  Jonggi Hong, Hernisa Kacorri · 2024 · ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper investigates how blind and low-vision users interact with object recognition systems, specifically focusing on how they identify and handle recognition errors. While object recognition technologies powered by computer vision and machine learning have enormous…

  blind users · object recognition · AI errors · computer vision · camera-based assistive technology

• Exploring Accessibility of Mobile Applications Through User Feedback: Insights from App Reviews in a Systematic Literature Review

  Alberto Dumont Alves Oliveira, Marcelo Medeiros Eler · 2024 · Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems (IHC 2024)

  This systematic literature review examines how user reviews from app stores have been used to study the accessibility of mobile applications. The authors searched five major academic databases (ACM, IEEE, ScienceDirect, Scopus, and Web of Science) and identified 638 papers,…

  mobile accessibility · app reviews · user feedback · systematic literature review · user reviews

• Stress Detection of Autistic Adults during Simulated Job Interviews Using a Novel Physiological Dataset and Machine Learning

  Miroslava Migovich, Deeksha Adiani, Michael Breen, Amy Swanson, Timothy J. Vogus, Nilanjan Sarkar · 2024 · ACM Transactions on Accessible Computing

  This research addresses the significant employment barriers faced by autistic adults, with job interviews identified as a primary obstacle. The researchers developed a physiological stress detection system integrated with CIRVR (Career Interview Readiness in Virtual Reality), a…

  autism · employment · job interview · physiological sensing · affective computing

• Measuring the Accuracy of Automatic Speech Recognition Solutions

  Korbinian Kuhn, Verena Kersken, Benedikt Reuter, Niklas Egger, Gottfried Zimmermann · 2024 · ACM Transactions on Accessible Computing

  This study provides independent, comprehensive benchmarking of 11 common automatic speech recognition (ASR) services to assess their real-world accuracy for accessibility purposes. The research addresses a critical gap: while vendors claim "state-of-the-art accuracy" and…

  automatic speech recognition · ASR · captions · deaf and hard of hearing · transcription

• UIClip: A Data-driven Model for Assessing User Interface Design

  Jason Wu, Yi-Hao Peng, Xin Yue Amanda Li, Amanda Swearngin, Jeffrey P. Bigham, Jeffrey Nichols · 2024 · Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST '24)

  This paper introduces UIClip, a computational model that automatically assesses UI design quality and visual relevance from a screenshot and natural language description. Built on OpenAI's CLIP B/32 architecture (151 million parameters), UIClip is fine-tuned on a novel…

  UI design · machine learning · design quality assessment · computer vision · CLIP

• Towards Automated Accessibility Report Generation for Mobile Apps

  Amanda Swearngin, Jason Wu, Xiaoyi Zhang, Esteban Gomez, Jen Coughenour, Rachel Stukenborg, Bhavya Garg, Greg Hughes, Adriana Hilliard, Jeffrey P. Bigham, Jeffrey Nichols · 2024 · ACM Transactions on Computer-Human Interaction

  This paper presents a system for automatically generating whole-app accessibility reports for mobile apps, addressing key limitations of existing accessibility scanning tools. The work begins with formative interviews with eight accessibility QA professionals at a large…

  automated testing · accessibility testing · mobile accessibility · app crawling · machine learning

9 results.