Gated Convolutional Fusion for Time-Domain Target Speaker Extraction Network

Target speaker extraction aims to extract the target speaker's voice from mixed utterances based on auxillary reference speech of the target speaker. A speaker embedding is usually extracted from the reference speech and fused with the learned acoustic representation. The majority of existing works perform simple operation-based fusion of concatenation. However, potential cross-modal correlation may not be effectively explored by this naive approach that directly fuse the speaker embedding into the acoustic representation. In this work, we propose a gated convolutional fusion approach by exploring global conditional modeling and trainable gating mechanism for learning sophisticated interaction between speaker embedding and acoustic representation. Experiments on WSJ0-2mix-extr dataset proves the efficacy of the proposed fusion approach, which performs favorably against other fusion methods with considerable improvement in terms of SDRi and SI-SDRi. Moreover, our method can be flexibly incorporated into similar time-domain speaker extraction networks to attain better performance.