A MAXIMUM LIKELIHOOD APPROACH TO MULTI-OBJECTIVE LEARNING USING GENERALIZED GAUSSIAN DISTRIBUTIONS FOR DNN-BASED SPEECH ENHANCEMENT

The multi-objective learning using minimum mean squared error criterion for DNN-based speech enhancement (MMSE-MOL-DNN) has been demonstrated to achieve better performance than single output DNN. However, one problem of MMSE-MOL-DNN is that the prediction error values on different targets have a very broad dynamic range, causing difficulty in DNN training. In this paper, we extend the maximum likelihood approach proposed in our previous work [1] to the multi-objective learning for DNN-based speech enhancement (ML-MOL-DNN) to achieve the automatic adjustment of the dynamic range of prediction error values on different targets. The conditional likelihood function to be maximized is derived under the generalized Gaussian distribution (GGD) error model. Moreover, the control of the dynamic range of the prediction error values on different targets is achieved by the scale factors in GGD. Furthermore, we propose a method to update the shape factors automatically utilizing the one-to-one mapping between the kurtosis and shape factor in GGD instead of manual adjustment. The experimental results show that our ML-MOL-DNN can achieve better performance than MMSE-MOL-DNN in terms of different objective measures.