Konstantinos Papadopoulos and Eleni Koustriava
2015
Accessibility, Greek, Discernment
The present study examines the comprehension of texts presented via synthetic and natural speech in individuals with and without visual impairments. Twenty adults with visual impairments and 65 sighted adults participated in the study.
Generation and Detection of Sign Language Deepfakes – A Linguistic and Visual Analysis
Shahzeb Nacem, Muhammad Riyyan Khan, Usman Tariq, Abhinav Dhall, Carlos Ivan Colon, Hasan Al-Nashash
2024
Generation, Discernment, Detection, Dataset, Accessibility, American Sign Language
This research presents a positive application of deepfake technology in upper body generation, while performing sign-language for the Deaf and Hard of Hearing (DHoH) community.
Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas
Aashaka Desai, Maartje De Meulder, Julie A. Hochgesang, Annemarie Kocab, and Alex X. Lu
2024
Accessibility, American Sign Language
This study conducts a systematic review of 101 recent papers in sign language AI. The analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models.
Chaeeun Han, Prasenjit Mitra, Syed Masum Billah
2024
Discernment, Accessibility, English
This paper explores how blind and sighted individuals perceive real and spoofed audio, highlighting differences and similarities between the groups.
Zero-Shot Voice Cloning Text-to-Speech for Dysphonia Disorder Speakers
Kurniawati Azizah
2024
Generation, Accessibility, English
This research enhances zero-shot voice cloning TTS for individuals with dysphonia, improving speaker similarity, intelligibility, and sound quality through adjustments in model architecture and loss functions. The optimized model shows notable improvements over the baseline in cosine similarity, character error rate, and mean opinion score, making dysphonic speech clearer and closer in quality to the original voices of speakers with dysphonia.