  A three-stage approach is proposed for speaker counting and speech separation
in noisy and reverberant environments. In the spatial feature extraction, a
spatial coherence matrix (SCM) is computed using whitened relative transfer
functions (wRTFs) across time frames. The global activity functions of each
speaker are estimated from a simplex constructed using the eigenvectors of the
SCM, while the local coherence functions are computed from the coherence
between the wRTFs of a time-frequency bin and the global activity
function-weighted RTF of the target speaker. In speaker counting, we use the
eigenvalues of the SCM and the maximum similarity of the interframe global
activity distributions between two speakers as the input features to the
speaker counting network (SCnet). In speaker separation, a global and local
activity-driven network (GLADnet) is used to extract each independent speaker
signal, which is particularly useful for highly overlapping speech signals.
Experimental results obtained from the real meeting recordings show that the
proposed system achieves superior speaker counting and speaker separation
performance compared to previous publications without the prior knowledge of
the array configurations.
