In this paper, we propose a new speech enhancement system using a deep neural network (DNN)-augmented colored-noise Kalman filter. In our system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the linear prediction coefficients (LPCs) and the driving noise variances. The LPCs are obtained through training a multi-objective DNN that learns the mapping from the noisy acoustic features to the line spectrum frequencies (LSFs), while the driving noise variances are obtained by solving an optimization problem aiming to minimize the difference between the modelled and observed AR spectra of the noisy speech. The colored-noise Kalman filter with DNN estimated parameters is then applied to the noisy speech for denoising. Finally, a post-subtraction technique is adopted to further remove the residual noise in the Kalman-filtered speech. Extensive computer simulations show that the proposed speech enhancement system achieves significant performance gains when compared to conventional Kalman filter based algorithms as well as recent DNN-based methods under both seen and unseen noise conditions.

在本文中，我们提出了一种使用深度神经网络（DNN）增强的彩色噪声卡尔曼滤波器的新型语音增强系统。在我们的系统中，干净的语音和噪声都被建模为自回归（AR）过程，其参数包括线性预测系数（LPC）和行驶噪声方差。LPC是通过训练多目标DNN来获得的，该DNN学习了从嘈杂的声学特征到线谱频率（LSF）的映射，而行驶噪声方差是通过解决优化问题而获得的，该问题旨在最大程度地减少建模噪声和线性噪声之间的差异。观察到嘈杂语音的AR光谱。然后将具有DNN估计参数的彩色噪声卡尔曼滤波器应用于带噪语音以进行降噪。最后，采用后减法技术以进一步去除卡尔曼滤波语音中的残留噪声。大量的计算机仿真表明，与常规的基于Kalman滤波器的算法以及最近的基于DNN的方法相比，所提出的语音增强系统在可见和不可见噪声条件下均​​实现了显着的性能提升。