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

Challenges and Insights: Exploring 3D Spatial Features and Complex Networks on the MISP Dataset