关键词: 语音增强, 稀疏低秩模型, 相位谱补偿, 归一化最小均方（NLMS）, 感知语音质量评价, 补偿因子

Abstract: Aiming at the problems that the existing speech enhancement algorithms have poor speech enhancement effect and speech signal distortion, a speech enhancement algorithm with sparse low rank model and improved phase spectrum compensation is proposed. First, the sparse low rank model is used to deal with the amplitude spectrum of the noisy speech, and the separated speech is obtained. Second, Normalized Least Mean Square（NLMS） adaptive filtering algorithm is applied to optimize the compensation factor of Phase Spectrum Compensation（PSC） algorithm. In addition, speech after the sparse low rank separation is processed by the improved phase spectrum compensation to obtain the enhanced speech. Finally, the Perceptual Evaluation of Speech Quality（PESQ） and spectrum are used to analyze the enhanced speech. The experimental results show that the noise can be effectively removed with the method, and in the case of low signal to noise ratio, the clarity of speech can be maintained with the method.

Key words: speech enhancement, sparse low rank model, phase spectrum compensation, Normalized Least Mean Square（NLMS）, perceptual evaluation of speech quality, compensation factor