Multi-eigenspace normalization for robust speech recognition in noisy environments

In this paper, we propose an effective feature normalization scheme based on eigenspace normalization, for achieving robust speech recognition. In general, Mean and Variance Normalization (MVN) is implemented in cepstral domain. However, another MVN approach using eigenspace was recently introduced, in that the eigenspace normalization procedure performs normalization in a single eigenspace. This procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In the proposed scheme, we apply independent and unique eigenspaces to cepstra, delta and delta-delta cepstra respectively. We also normalize training data in eigenspace. In addition, a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial improvement over the basic eigenspace normalization.