Speech Enhancement Based on NMF Under Electric Vehicle Noise Condition

Abstract

Speech-based human-machine interaction (HMI) is essential to electronic navigation, autonomous cars, and intelligent vehicles. The noises generated by the mechanical motion or electric power equipment degrade speech quality and result in HMI failing to work effectively. However, there is relatively little literature available on speech enhancement under electric vehicle noise condition. This paper presents a speech enhancement method based on improved nonnegative matrix factorization (ImNMF). Unlike the traditional nonnegative matrix factorization (NMF) trains its speech dictionary using speech recorded in advance which inevitably contains a little noise component, ImNMF generates the speech dictionary using the spectra of pitch and their harmonics via mathematical model. This purpose is to guarantee the purity of speech dictionary. In addition, in order to alleviate the loss of the information of the noise sample, ImNMF constructs noise dictionary by a combination of the gain adjusted spectrum frames of the noise samples separated online. Compared with traditional NMF, the ImNMF noise atoms are relatively larger. Thus, the representation of speech signal mixed with noise atoms is greatly reduced. Therefore, ImNMF can reduce distortion of reconstructed speech while enhancing the recovered speech quality. Speech enhancement and speaker verification experiments on NUST603 and TIMIT data showed that the proposed ImNMF can effectively enhance speech signal in the noise environment of electric vehicles and further can reduce the equal error rate of the speaker verification system.

Keywords

• Electric vehicle noise
• speech enhancement
• non-negative matrix factorization
• speaker verification