关键词: 语音增强, 非平稳噪声, 隐马尔可夫模型, 高斯混合模型

Abstract:

Because the existing single channel speech enhancement technologies perform not well in the tracking and suppression of non-stationary noise, the speech enhancement method based on online energy adjustment is proposed.The normalized critical band energy parameters are employed as the feature in Gaussian mixture model (GMM) to distinguish the background noises.Based on the AR-HMM of clean speech and the noise of corresponding type, the power spectrums of speech and noise are estimated under minimum mean square error (MMSE) criteria.When the differences between the training data and test data are considered in the non-stationary noise environment, the online adjustment method for the speech and noise models is necessary.The scaling factor of speech energy is estimated with the iterative expectation maximization (EM) algorithm and the one of noise energy is estimated with the re-estimation approach similar to the training stage.And the initial scaling factor of noise energy is obtained by minima-controlled recursive averaging (MCRA) algorithm.The evaluation of the proposed method is performed under the standard of ITU-T G.160.The test results reveal that, comparing with the two reference methods, the proposed method performs well in non-stationary noise environments, including larger noise reduction and shorter convergence time.

Key words: speech enhancement, non-stationary noise, hidden Markov model, Gaussian mixture model