  Automatic speech recognition (ASR) of multi-channel multi-speaker overlapped
speech remains one of the most challenging tasks to the speech community. In
this paper, we look into this challenge by utilizing the location information
of target speakers in the 3D space for the first time. To explore the strength
of proposed the 3D spatial feature, two paradigms are investigated. 1) a
pipelined system with a multi-channel speech separation module followed by the
state-of-the-art single-channel ASR module; 2) a "All-In-One" model where the
3D spatial feature is directly used as an input to ASR system without explicit
separation modules. Both of them are fully differentiable and can be
back-propagated end-to-end. We test them on simulated overlapped speech and
real recordings. Experimental results show that 1) the proposed ALL-In-One
model achieved a comparable error rate to the pipelined system while reducing
the inference time by half; 2) the proposed 3D spatial feature significantly
outperformed (31\% CERR) all previous works of using the 1D directional
information in both paradigms.
