Enhancing Block-Online Speech Separation using Interblock Context Flow
Conggui Liu, Yoshinao Sato · 2021
Despite recent progress in speech separation, online processing is still challenging. One promising approach is the block-online structure, which has been examined in a few previous studies. However, in a blockwise model, the available context information is limited to the same block. To overcome this limitation, we investigate the enhancement of a block-online speech separation model using interblock context flow. Specifically, we propose a blockwise temporal convolution network with layers between adjacent blocks that allow the propagation of interblock context information. We evaluate this model on single-channel speech mixtures with different context widths, latencies, and intervals in noisy and reverberant environments generated by the image method using the Wall Street Journal 0 corpus. The experimental results indicate that the proposed model outperforms the baseline blockwise model under all the conditions.