Single Channel Speech Separation

Single-channel speech separation aims to isolate individual voices from a single-microphone recording containing overlapping speech, a crucial task for improving speech recognition and human-computer interaction in noisy environments. Current research focuses on developing computationally efficient models, such as lightweight Transformers and modified Conv-TasNets, that address the limitations of resource-intensive architectures while maintaining high accuracy, particularly in challenging conditions like reverberation and similar-pitch speakers. Efforts also concentrate on enhancing the perceptual quality of separated speech and improving robustness to mismatched training and testing conditions, leveraging techniques like diffusion models and refined permutation invariant training. These advancements have significant implications for applications ranging from hearing aids and voice assistants to meeting transcription and robotics.