提出基于归一化ℓp范数的麦克风选择方法，提升远场语音识别效果。
Reference Microphone Selection for Guided Source Separation based on the Normalized L-p Norm
- 用归一化ℓp范数评估麦克风信号质量，兼顾信噪比与混响特性
- 在CHiME-8数据集上降低平均词错误率，优于传统信噪比方法
- 适合需要高鲁棒性的远场语音识别系统使用
引导源分离（GSS）是基于空间分布麦克风的远场自动语音识别（ASR）系统的常用前端。在使用空间分布麦克风时，参考麦克风的选择对输出信号质量和下游ASR性能有显著影响。当前的GSS语音增强通常采用信噪比（SNR）进行参考麦克风选择，虽利于降噪，但可能忽略各麦克风间早期/晚期混响比（ELR）的差异。本文提出两种基于归一化ℓp范数的参考麦克风选择方法：一种仅使用归一化ℓp范数，另一种结合归一化ℓp范数与SNR，以同时考虑信噪比和混响特性差异。在CHiME-8远场ASR系统上的实验表明，所提ℓp范数方法优于基线方法，显著降低了宏平均词错误率。
原文摘要 · Abstract (English)
Guided Source Separation (GSS) is a popular front-end for distant automatic speech recognition (ASR) systems using spatially distributed microphones. When considering spatially distributed microphones, the choice of reference microphone may have a large influence on the quality of the output signal and the downstream ASR performance. In GSS-based speech enhancement, reference microphone selection is typically performed using the signal-to-noise ratio (SNR), which is optimal for noise reduction but may neglect differences in early-to-late-reverberant ratio (ELR) across microphones. In this paper, we propose two reference microphone selection methods for GSS-based speech enhancement that are based on the normalized $\ell_p$-norm, either using only the normalized $\ell_p$-norm or combining the normalized $\ell_p$-norm and the SNR to account for both differences in SNR and ELR across microphones. Experimental evaluation using a CHiME-8 distant ASR system shows that the proposed $\ell_p$-norm-based methods outperform the baseline method, reducing the macro-average word error rate.
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