Speech Processing Task

Speech processing research focuses on developing efficient and accurate methods for automatically understanding and manipulating spoken language. Current efforts concentrate on improving model architectures like Transformers and Conformers, leveraging self-supervised learning techniques (e.g., HuBERT, WavLM), and exploring innovative approaches such as prompting and parameter-efficient fine-tuning (e.g., adapters) to enhance performance across diverse tasks (speech recognition, speaker verification, emotion recognition, etc.). These advancements are driving progress in various applications, including virtual assistants, healthcare diagnostics, and multilingual communication technologies, by enabling more robust and resource-efficient systems.