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
Examining the Interplay Between Privacy and Fairness for Speech Processing: A Review and Perspective
Anna Leschanowsky, Sneha Das
Speech Recognition Transformers: Topological-lingualism Perspective
Shruti Singh, Muskaan Singh, Virender Kadyan
Feature Representations for Automatic Meerkat Vocalization Classification
Imen Ben Mahmoud, Eklavya Sarkar, Marta Manser, Mathew Magimai. -Doss