  Multi-channel speech separation using speaker's directional information has
demonstrated significant gains over blind speech separation. However, it has
two limitations. First, substantial performance degradation is observed when
the coming directions of two sounds are close. Second, the result highly relies
on the precise estimation of the speaker's direction. To overcome these issues,
this paper proposes 3D features and an associated 3D neural beamformer for
multi-channel speech separation. Previous works in this area are extended in
two important directions. First, the traditional 1D directional beam patterns
are generalized to 3D. This enables the model to extract speech from any target
region in the 3D space. Thus, speakers with similar directions but different
elevations or distances become separable. Second, to handle the speaker
location uncertainty, previously proposed spatial feature is extended to a new
3D region feature. The proposed 3D region feature and 3D neural beamformer are
evaluated under an in-car scenario. Experimental results demonstrated that the
combination of 3D feature and 3D beamformer can achieve comparable performance
to the separation model with ground truth speaker location as input.
