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

• URL: http://arxiv.org/abs/2311.00146v2
• Date: Tue, 11 Jun 2024 22:09:26 GMT
• Title: RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios
• Authors: Yiwen Shao, Shi-Xiong Zhang, Dong Yu,
• Abstract summary: We introduce RIR-SF, a novel spatial feature based on room impulse response (RIR) RIR-SF significantly outperforms traditional 3D spatial features, showing superior theoretical and empirical performance. We also propose an optimized all-neural multi-channel ASR framework for RIR-SF, achieving a relative 21.3% reduction in CER for target speaker ASR in multi-channel settings.
• Score: 36.50731790624643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic speech recognition (ASR) on multi-talker recordings is challenging. Current methods using 3D spatial data from multi-channel audio and visual cues focus mainly on direct waves from the target speaker, overlooking reflection wave impacts, which hinders performance in reverberant environments. Our research introduces RIR-SF, a novel spatial feature based on room impulse response (RIR) that leverages the speaker's position, room acoustics, and reflection dynamics. RIR-SF significantly outperforms traditional 3D spatial features, showing superior theoretical and empirical performance. We also propose an optimized all-neural multi-channel ASR framework for RIR-SF, achieving a relative 21.3\% reduction in CER for target speaker ASR in multi-channel settings. RIR-SF enhances recognition accuracy and demonstrates robustness in high-reverberation scenarios, overcoming the limitations of previous methods.

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