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.

翻译：多通道多说话人语音识别在语音处理领域面临着严峻挑战，其典型问题包括背景噪声、混响和语音重叠。克服这些复杂性需要利用上下文线索，从嘈杂的混合声中分离出目标语音，从而实现精准识别。在这些线索中，3D空间特征已成为前沿解决方案，尤其是在配备目标说话人空间信息时。该特征在混合音频中辨别目标说话人的卓越能力，通常使得中间处理步骤变得多余，为直接训练“一体化”自动语音识别（ASR）模型铺平了道路。这些模型在模拟数据和真实数据上均展现了可观的性能。在本文中，我们将该方法扩展到MISP数据集以进一步验证其有效性。我们深入探讨了在MISP上应用3D空间特征时遇到的挑战与获得的见解，同时开展了将这一特征替换为更复杂输入与模型的初步实验探索。