Speech System

Speech systems research focuses on improving the accuracy and efficiency of technologies that process and generate human speech, encompassing tasks like automatic speech recognition (ASR), text-to-speech (TTS), and voice conversion. Current research emphasizes developing robust models, often employing deep learning architectures like ECAPA-TDNN and transformer-based networks, to handle diverse accents, low-resource languages, and noisy environments, often leveraging techniques like transfer learning and synthetic data augmentation. These advancements are crucial for applications ranging from oral history preservation and language revitalization to improving accessibility in online conferencing and creating more inclusive speech technologies.