MBZUAI student Karima Kadaoui is developing machine learning algorithms to help speech-impaired individuals communicate more easily. Her project aims to create an app that translates speech impediments into understandable language, facilitating communication with others and integration with voice-enabled technologies like Siri and Google Assistant. The AI-powered app could assist individuals with conditions such as strokes and cerebral palsy, who often struggle with muscle control affecting speech clarity. Why it matters: The research addresses a critical need for inclusive AI solutions, potentially improving the quality of life for speech-impaired individuals in the region and beyond.
MBZUAI's Hanan Al Darmaki is working to improve automated speech recognition (ASR) for low-resource languages, where labeled data is scarce. She notes that Arabic presents unique challenges due to dialectal variations and a lack of written resources corresponding to spoken dialects. Al Darmaki's research focuses on unsupervised speech recognition to address this gap. Why it matters: Overcoming these challenges can improve virtual assistant effectiveness across diverse languages and enable more inclusive AI applications in the Arabic-speaking world.
MBZUAI doctoral student Hawau Toyin is applying AI to the identification, correction, and evaluation of stuttering, particularly in developing countries where it often goes undiagnosed. She is collaborating with the SpeechCare Center UAE and her advisor Dr. Hanan Aldarmaki to develop AI tools for faster and more accessible diagnosis and treatment. The research focuses on data collection from around the world to build an effective AI system that can analyze the various forms of stuttering. Why it matters: This research addresses a critical healthcare gap by leveraging AI to improve diagnosis and treatment of speech disorders in underserved regions.
Researchers at MBZUAI have developed Auto-DUB, a system using deep learning, NLP, and CV to improve audio-visual dubbing, particularly for educational videos. The three-step process generates subtitles, creates an audio representation, and synchronizes the audio with lip movements. The system aims to overcome language barriers in e-learning by providing accurate translations and lip-synced audio. Why it matters: This research addresses a critical need in online education by making content more accessible to non-native English speakers, potentially expanding access to global educational resources in the Arab world.
MBZUAI researchers have developed an AI program using vision transformers that can learn a person's handwriting style and generate text in that style. The US Patent and Trademark Office recently granted a patent for this technology, which could aid individuals with writing impairments. The system overcomes limitations of previous GAN-based approaches by processing long-range dependencies in handwriting. Why it matters: This patented AI tool enhances personalized text generation and has potential applications in assistive technology and improving handwriting recognition models.