Compact Network for Speakerbeam Target Speaker Extraction

Speech separation that separates a mixture of speech signals into each of its sources has been an active research topic for a long time and has seen recent progress with the advent of deep learning. A related problem is target speaker extraction, i.e. extraction of only speech of a target speaker out of a mixture, given characteristics of his/her voice. We have recently proposed SpeakerBeam, which is a neural network-based target speaker extraction method. Speaker-Beam uses a speech extraction network that is adapted to the target speaker using auxiliary features derived from an adaptation utterance of that speaker. Initially, we implemented SpeakerBeam with a factorized adaptation layer, which consists of several parallel linear transformations weighted by weights derived from the auxiliary features. The factorized layer is effective for target speech extraction, but it requires a large number of parameters. In this paper, we propose to simply scale the activations of a hidden layer of the speech extraction network with weights derived from the auxiliary features. This simpler approach greatly reduces the number of model parameters by up to 60%, making it much more practical, while maintaining a similar level of performance. We tested our approach on simulated and real noisy and reverberant mixtures, showing the potential of SpeakerBeam for real-life applications. Moreover, we showed that speech extraction performance of SpeakerBeam compares favorably with that of a state-of-the-art speech separation method with a similar network configuration.