Speech Separation Model

Speech separation models aim to isolate individual voices from a mixture of sounds, a crucial task for applications like automatic speech recognition in noisy environments. Current research focuses on improving model efficiency (e.g., reducing computational cost and parameters) and generalization ability across diverse real-world acoustic conditions, employing architectures like Transformers and Convolutional networks with techniques such as permutation invariant training. These advancements are significant for enhancing the robustness and practicality of speech processing systems, particularly in scenarios with overlapping speech, noise, and reverberation.