Accent Recognition

Accent recognition, the automated identification of speaker accents, aims to improve speech processing systems' robustness and inclusivity by addressing biases towards standard accents. Current research focuses on developing deep learning models, including transformer-based architectures and DenseNets, often incorporating multi-task learning and techniques like adversarial training to disentangle speaker identity from accent features, and leveraging self-supervised learning for improved feature extraction. These advancements are crucial for building more equitable speech recognition and text-to-speech systems, impacting applications ranging from forensic phonetics to language learning and accessibility technologies.