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Bragg, D., Huynh, N. and Ladner, R.E. (2016) A Personalizable Mobile Sound Detector App Design for Deaf and Hard-of-Hearing Users. Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, Reno, 23-26 October 2016, 3-13.
https://doi.org/10.1145/2982142.2982171

has been cited by the following article:

• TITLE: A Machine Learning-Based App for Sound Detection and Speech Recognition for the Deaf and Hard of Hearing

  AUTHORS: Nada Barnawi, Mohammed Alnuem

  KEYWORDS: Machine Learning-Based, Deaf and Hard of Hearing, Sound Detection, Speech Recognition, Realtime, Android Application, Assistive Technologies, Saudi Arabia

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.8, August 25, 2025

  ABSTRACT: This project uses AI to improve safety and communication for the deaf and hard-of-hearing community in Saudi Arabia. By combining real-time sound detection and speech recognition, it offers alerts for potential dangers and supports communication. The system is built with Android Studio, TensorFlow Lite, and Speech-to-Text APIs. The core model uses YAMNet for feature extraction and a Deep Neural Network (DNN) for classification, achieving 95.77% accuracy. Future updates will include features like customizable alerts, hazard detection, sign language support, and location tracking, aiming to enhance safety and inclusivity.