改进的后滤波波束形成器语音增强算法

该文提出了一种具有后滤波的波束形成器的语音增强改进算法。该算法主要解决维纳滤波器的理想信号功率谱估计，结合自功率谱减法和互功率谱减法计算出尽可能多的功率谱估计值，以使平均结果更接近于真实值，同时修正了声源移动引起的互功率谱变化。实验结果信噪比提高5dB以上，汽车环境中基于隐含马尔可夫模型(HMM)的小词汇量短语识别达到84％。从信噪比、平均谱距离和语音识别率可以看出该算法有效去除了原始算法中易残留的低频噪声，减少了语音信号失真。

The Modified Post-Filter Beamforming for Speech Enhancement

A modified Wiener post-filter beamforming for speech enhancement is presented. The proposed algorithm focuses on the estimation of the ideal signal’s power spectrum for the Wiener filter, both auto-correlation and cross-correlation are taken into consideration to obtain as more power spectrum estimations as possible, the average of which produces more accurate result. Meanwhile, the alteration of cross-correlation caused by the moving speaker is revised. The performance of the algorithm has been evaluated from Signal to Noise Ratio (SNR), Average Log Spectrum Distance (ALSD) and speech recognition rate. The SNR is improved by 5 dB. The command recognition rate based on Hidden Markov Model (HMM) in the car environment reaches 84%. The low frequency noise that still remains after the process of the primary method is reduced by the proposed method, and the speech signal distortion is also decreased.