Joint Speaker Feature

Joint speaker feature learning aims to improve speech processing systems by integrating speaker-specific information directly into model training. Current research focuses on incorporating speaker embeddings, often derived from architectures like xVectors or ECAPA-TDNN, into multi-channel speech separation, recognition, and cross-lingual text-to-speech systems, often using multi-task learning or joint training with speaker classifiers. These methods demonstrate significant improvements in performance metrics like Word Error Rate (WER) and subjective evaluations of speaker similarity, particularly in challenging scenarios like multi-talker speech and cross-lingual synthesis. This work has implications for enhancing the robustness and accuracy of various speech technologies.