Research on monaural speech segregation based on feature selection

Abstract

Abstract Speech feature model is the basis of speech and noise separation, speech expression, and different styles of speech conversion. With the development of signal processing methods, the feature types and dimensions increase. Therefore, it is difficult to select appropriate features. If a single feature is used, the representation of the speech signal will be incomplete. If multiple features are used, there will be redundancy between features, which will affect the performance of speech separation. The feature described above is a combination of parameters to characterize speech. A single feature means that the combination has only one parameter. In this paper, the feature selection method is used to select and combine eight widely used speech features and parameters. The Deep Neural Network (DNN) is used to evaluate and analyze the speech separation effect of different feature groups. The comparison results show that the speech segregation effect of the complementary feature group is better. The effectiveness of the complementary feature group to improve the performance of DNN speech separation is verified.

Keywords

• Feature selection
• Group lasso
• Deep Neural Network (DNN)
• Monaural speech segregation
• Complementary feature group