About Me

*** More updates forthcoming! Please refer to my CV for current info!

Abraham Glasser, PhD, is an Assistant Professor (since 2023) in the School of Science, Technology, Accessibility, Mathematics, and Public Health (STAMP) at Gallaudet University in Washington, D.C.

He previously completed internships at Microsoft and at the NASA Kennedy Space Center, and he has conducted research at the NTID Center on Access Technology and through several NSF Research Experiences for Undergraduate programs (twice as a student and once as a graduate mentor). He also interned with Microsoft Research in 2020 and Google Research in 2022. He is the recipient of an Honorable Mention in the 2018 National Science Foundation Graduate Research Fellowship program, and he was the first-place winner in the Student Research Competition at the ACM CHI Conference on Human Factors in Computing Systems, and was awarded Best Poster at the ACM VRST Virtual Reality Software and Technology Symposium. He has published at several venues, including the ACM CHI, ASSETS, W4A, VRST, and CUI conferences.

His research investigates technologies for people who are Deaf or Hard of Hearing, including speech and sign language technology.

*** More updates forthcoming! Please refer to my CV for current info!

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Works
2021
C.19COMING SOON: Effect of Caption Width on the TV User Experience by Deaf and Hard of Hearing Viewers
C.18COMING SOON: Understanding Deaf and Hard-of-Hearing Users’ Interest in Sign-Language Interaction with Personal-Assistant Devices
C.17Deaf Users’ Preferences Among Wake-Up Approaches during Sign-Language Interaction with Personal Assistant Devices
2020
C.16On How Deaf and Hard of Hearing Users Might Use Sign Language Conversational User Interfaces
O.3Experiences of Computing Students with Disabilities
C.15Automatic text simplification tools for deaf and hard of hearing adults: benefits of lexical simplification and providing users with autonomy
C.14Accessibility for deaf and hard of hearing users: sign language conversational user interfaces
J.1Failed Power Domination
2019
C.13Mixed Reality Speaker Identification As an Accessibility Tool for Deaf and Hard of Hearing Users
Best Poster Award
C.12Artificial Intelligence Fairness in the Context of Accessibility Research on Intelligent Systems for People who are Deaf or Hard of Hearing
C.11Effect of Automatic Sign Recognition Performance on the Usability of Video-Based Search Interfaces for Sign Language Dictionaries
C.10Designing an Interface to Support the Creation of Animations of Individual ASL Signs
C.9, P.12, P.11Automatic Speech Recognition Services: Deaf and Hard-of-Hearing Usability
1st Place - Undergraduate Student Research Competition
O.2, P.10Failed Power Domination on Knödel Graphs
2018
P.9Collection of Training Data for a Video-Based Search Tool for ASL Dictionaries
P.8Preferences and Requirements of Deaf and Hard-of-Hearing Users for Captions Generated via Automatic Speech Recognition
P.7Adapting Reading-Assistance and Automatic Text-Simplification Tools to Assist Self-Directed Learning by Deaf and Hard-of-Hearing Computing Workers
P.6Video and 3D Depth Training Data Collection for Sign-Language Computer Vision Models, in Support of Linguistics Research
O.1Failed Power Domination: Computational Results, Extreme Values, and Complexity
P.4Evaluating the Use of Automatic Speech Recognition for Lectures with Multiple Modalities
P.3Mathematical determination of the worst case scenario for monitoring electric power networks
C.8RTTD-ID Tracked Captions with Multiple Speakers for Deaf Students
C.7A Transition Community for Deaf and Hard of Hearing Students in Engineering Programs
2017
C.6, P.5Feasibility of Using Automatic Speech Recognition with Voices of Deaf and Hard-of-Hearing Individuals
C.5Deaf, Hard of Hearing, and Hearing Perspectives on using Automatic Speech Recognition in Conversation
P.2Sign Language Recognition Using the Kinect
C.4Closed ASL Interpreting for Online Videos
C.3Automatic Speech Recognition and Readability
C.2Comparing Automatic Speech Recognition Word Error Rates for Speech & Signs
C.1Usability Testing of Mobile ASR Applications
2016
P.1Automatic Speech Recognition Relationship Between Text Readability and Word Error Rate