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Automatic speech recognition (ASR) on multi-talker recordings is challenging.Current methods using 3D spatial data from multi-channel audio and visual cuesfocus mainly on direct waves from the target speaker, overlooking reflectionwave impacts, which hinders performance in reverberant environments. Ourresearch introduces RIR-SF, a novel spatial feature based on room impulseresponse (RIR) that leverages the speaker's position, room acoustics, andreflection dynamics. RIR-SF significantly outperforms traditional 3D spatialfeatures, showing superior theoretical and empirical performance. We alsopropose an optimized all-neural multi-channel ASR framework for RIR-SF,achieving a relative 21.3\% reduction in CER for target speaker ASR inmulti-channel settings. RIR-SF enhances recognition accuracy and demonstratesrobustness in high-reverberation scenarios, overcoming the limitations ofprevious methods.

RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios