  In this paper, we present the Blind Speech Separation and Dereverberation
(BSSD) network, which performs simultaneous speaker separation, dereverberation
and speaker identification in a single neural network. Speaker separation is
guided by a set of predefined spatial cues. Dereverberation is performed by
using neural beamforming, and speaker identification is aided by embedding
vectors and triplet mining. We introduce a frequency-domain model which uses
complex-valued neural networks, and a time-domain variant which performs
beamforming in latent space. Further, we propose a block-online mode to process
longer audio recordings, as they occur in meeting scenarios. We evaluate our
system in terms of Scale Independent Signal to Distortion Ratio (SI-SDR), Word
Error Rate (WER) and Equal Error Rate (EER).
