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 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.
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.
A research talk was given on privacy and security issues in speech processing, highlighting the unique privacy challenges due to the biometric information embedded in speech. The talk covered the legal landscape, proposed solutions like cryptographic and hashing-based methods, and adversarial processing techniques. Dr. Bhiksha Raj from Carnegie Mellon University, an expert in speech and audio processing, delivered the talk. Why it matters: As speech-based interfaces become more prevalent in the Middle East, understanding and addressing the associated privacy risks is crucial for ethical AI development and deployment.
This survey paper reviews the landscape of Natural Language Processing (NLP) research and applications in the Arab world. It discusses the unique challenges posed by the Arabic language, such as its morphological complexity and dialectal diversity. The paper also presents a historical overview of Arabic NLP and surveys various research areas, including machine translation, sentiment analysis, and speech recognition. Why it matters: The survey provides a comprehensive resource for researchers and practitioners interested in the current state and future directions of Arabic NLP, a field critical for enabling AI technologies to serve Arabic-speaking communities.
The Qatar Computing Research Institute (QCRI) has released QASR, a 2,000-hour transcribed Arabic speech corpus collected from Aljazeera news broadcasts. The dataset features multi-dialect speech sampled at 16kHz, aligned with lightly supervised transcriptions and linguistically motivated segmentation. QCRI also released a 130M word dataset to improve language model training. Why it matters: QASR enables new research in Arabic speech recognition, dialect identification, punctuation restoration, and other NLP tasks for spoken data.
Pedro J. Moreno, former head of ASR R&D at Google, presented a talk at MBZUAI on the past, present, and future of speech technologies. The talk covered the evolution of speech tech, his career contributions including work on Google Voice search, and the impact of LLMs on speech science. He also discussed the interplay between foundational and applied research and preparing the next generation of scientists. Why it matters: The talk provides insights into the trajectory of speech technologies from a leading researcher, highlighting future directions and the ethical considerations surrounding AI's impact on society.
Thamar Solorio from the University of Houston will discuss machine learning approaches for spontaneous human language processing. The talk will cover adapting multilingual transformers to code-switching data and using data augmentation for domain adaptation in sequence labeling tasks. Solorio will also provide an overview of other research projects at the RiTUAL lab, focusing on the scarcity of labeled data. Why it matters: This presentation addresses key challenges in Arabic NLP related to data scarcity, which is a persistent obstacle in developing effective AI applications for the region.