基于复高斯混合模型的鲁棒VAD算法

Robust Voice Activity Detection Algorithm Based on Complex Gaussian Mixture Model

针对语音激活检测的鲁棒性问题,提出在非平稳噪声环境下使用基于复高斯混合模型的鲁棒语音激活检测算法.算法中假设纯净语音谱满足复高斯混合模型,先验信噪比利用预先训练好的复高斯混合模型计算得到.复高斯混合模型的引入一方面提高了语音激活检测的性能,另一方面避免了使用基于最小均方误差语音增强的先验信噪比估计过程.实验中使用NOISEX-92噪声库来验证系统在噪声环境下的性能.结果表明,该种算法在非平稳噪声环境下具有良好的检测性能.

In order to improve the robustness of voice activity detection （VAD）,the use of an algorithm based on complex Gaussian mixture model under nonstationary noisy environments was presented. In the algorithm,the clean speech distribution was modelled by complex Gaussian mixture model, and the a priori SNR was estimated based on the pre-trained complex Gaussian mixture model. The introduction of complex Gaussian mixture model not only improved the performance of voice activity detection,but also avoided the estimation of a priori SNR using minimum mean square error short spectral amplitude estimator. The system performance under noisy environments was evaluated using NOISEX-92 database. Experimental results show that the algorithm can work more robustly under nonstationary noisy environments.