  We investigate the effect of speaker localization on the performance of
speech recognition systems in a multispeaker, multichannel environment. Given
the speaker location information, speech separation is performed in three
stages. In the first stage, a simple delay-and-sum (DS) beamformer is used to
enhance the signal impinging from the speaker location which is then used to
estimate a time-frequency mask corresponding to the localized speaker using a
neural network. This mask is used to compute the second order statistics and to
derive an adaptive beamformer in the third stage. We generated a multichannel,
multispeaker, reverberated, noisy dataset inspired from the well studied
WSJ0-2mix and study the performance of the proposed pipeline in terms of the
word error rate (WER). An average WER of $29.4$% was achieved using the ground
truth localization information and $42.4$% using the localization information
estimated via GCC-PHAT. The signal-to-interference ratio (SIR) between the
speakers has a higher impact on the ASR performance, to the extent of reducing
the WER by $59$% relative for a SIR increase of $15$ dB. By contrast,
increasing the spatial distance to $50^\circ$ or more improves the WER by $23$%
relative only
