MFCC enhancement using joint corrupted and noise feature space for highly non-stationary noise environments

One of the most effective approaches to noise robust speech recognition is to remove the noise effect directly from corrupted MFCC vectors. However, VTS enhancement, which is a typical method for performing MFCC enhancement, provides limited improvement when the noise is highly non-stationary. This is because the VTS enhancement method cannot use a time-varying noise model to keep the computational cost at an acceptable level. This paper proposes a method that can enhance MFCC vectors and their dynamic parameters by using noise estimates that change on a frame-by-frame basis at a practical computational cost. The proposed method employs stereo data-based feature mapping like the well known SPLICE algorithm. The novelty of the proposed method lies in that it uses the joint space spanned by a concatenated vector of corrupted and noise features. It is also proposed to use linear discriminant analysis to effectively reduce the dimensionality of the joint space. The proposed method achieves 19.1% and 8.3% relative error reduction from the SPLICE and noise-mean normalized SPLICE algorithms, respectively.