  Multi-channel multi-talker speech recognition presents formidable challenges
in the realm of speech processing, marked by issues such as background noise,
reverberation, and overlapping speech. Overcoming these complexities requires
leveraging contextual cues to separate target speech from a cacophonous mix,
enabling accurate recognition. Among these cues, the 3D spatial feature has
emerged as a cutting-edge solution, particularly when equipped with spatial
information about the target speaker. Its exceptional ability to discern the
target speaker within mixed audio, often rendering intermediate processing
redundant, paves the way for the direct training of "All-in-one" ASR models.
These models have demonstrated commendable performance on both simulated and
real-world data. In this paper, we extend this approach to the MISP dataset to
further validate its efficacy. We delve into the challenges encountered and
insights gained when applying 3D spatial features to MISP, while also exploring
preliminary experiments involving the replacement of these features with more
complex input and models.
