Speech enhancement using PCA and variance of the reconstruction error model identification

We present in this paper a subspace approach for enhancing a noisy speech signal. The original algorithm for model identification from which we have derived our method has been used in the field of fault detection and diagnosis. This algorithm is based on principal component analysis in which the optimal subspace selection is provided by a variance of the reconstruction error (VRE) criterion. This choice overcomes many limitations encountered with other selection criteria, like overestimation of the signal subspace or the need for empirical parameters. We have also extended our subspace algorithm to take into account the case of colored and babble noise. The performance evaluation, which is made on the Aurora database shows that our method provides a higher noise reduction and a lower signal distortion than existing enhancement methods. Our algorithm succeeds in enhancing the noisy speech in all noisy conditions without introducing artifacts such as “musical noise”.