The information on the spatial location of speakers can be effectively used for multi-channel speaker separation. For example, Location-Based Training (LBT) uses the order of azimuth angles and distances of speakers to solve the permutation ambiguity problem. This location information can be used to improve the separation performance further. This paper proposes a multitask learning approach, Multitask Speaker Separation and Direction-of-Arrival Estimation Training (MSDET), jointly optimizing speaker separation and Direction-of-Arrival (DoA) estimation. In our evaluation using SMS-WSJ dataset, it outperforms LBT by 0.13 points in SI-SDR and 0.35 points in ESTOI.
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