  In this letter, we present a novel multi-talker minimum variance
distortionless response (MVDR) beamforming as the front-end of an automatic
speech recognition (ASR) system in a dinner party scenario. The CHiME-5 dataset
is selected to evaluate our proposal for overlapping multi-talker scenario with
severe noise. A detailed study on beamforming is conducted based on the
proposed extended complex Gaussian mixture model (CGMM) integrated with various
speech separation and speech enhancement masks. Three main changes are made to
adopt the original CGMM-based MVDR for the multi-talker scenario. First, the
number of Gaussian distributions is extended to 3 with an additional inference
speaker model. Second, the mixture coefficients are introduced as a supervisor
to generate more elaborate masks and avoid the permutation problems. Moreover,
we reorganize the MVDR and mask-based speech separation to achieve both noise
reduction and target speaker extraction. With the official baseline ASR
back-end, our front-end algorithm gained an absolute WER reduction of 13.87%
compared with the baseline front-end.
