SOUTY: A Voice Identity-Preserving Mobile Application for Arabic-Speaking Amyotrophic Lateral Sclerosis Patients Using Eye-Tracking and Speech Synthesis

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven by demographic shifts in aging populations, and the lifetime risk of developing ALS is 1 in 350–420. Despite international advancements in assistive technologies, a recent national survey in Saudi Arabia revealed that 100% of ALS care providers lack access to eye-tracking communication tools, and 92% reported communication aids as inconsistently available. While assistive technologies such as speech-generating devices and gaze-based control systems have made strides in recent decades, they primarily support English speakers, leaving Arabic-speaking ALS patients underserved. This paper presents SOUTY, a cost-effective, mobile-based application that empowers ALS patients to communicate using gaze-controlled interfaces combined with a text-to-speech (TTS) feature in Arabic language, which is one of the five most widely spoken languages in the world. SOUTY (i.e., “my voice”) utilizes a personalized, pre-recorded voice bank of the ALS patient and integrated eye-tracking technology to support the formation and vocalization of custom phrases in Arabic. This study describes the full development life cycle of SOUTY from conceptualization and requirements gathering to system architecture, implementation, evaluation, and refinement. Validation included expert interviews with Human–Computer Interaction (HCI) expertise and speech pathology specialty, as well as a public survey assessing awareness and technological readiness. The results support SOUTY as a culturally and linguistically relevant innovation that enhances autonomy and quality of life for Arabic-speaking ALS patients. This approach may serve as a replicable model for developing inclusive Augmentative and Alternative Communication (AAC) tools in other underrepresented languages. The system achieved 100% task completion during internal walkthroughs, with mean phrase selection times under 5 s and audio playback latency below 0.3 s.