Speech Processing
Speech processing research aims to enable computers to understand, interpret, and generate human speech, focusing on tasks like speech recognition, synthesis, and enhancement. Current efforts concentrate on improving model efficiency (e.g., using linear-complexity attention mechanisms) and robustness across diverse languages and acoustic conditions, often leveraging large language models and self-supervised learning techniques. These advancements are crucial for broader accessibility of speech technology, impacting fields ranging from healthcare (e.g., depression screening) to assistive technologies and improving human-computer interaction.
Papers
Biologically inspired speech emotion recognition
Reza Lotfidereshgi, Philippe Gournay
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR
Andrea DeMarco, Carlos Mena, Albert Gatt, Claudia Borg, Aiden Williams, Lonneke van der Plas
Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease
Adedolapo Aishat Toye, Suryaprakash Kompalli