Speech enhancement using PCA and variance of the reconstruction error in distributed speech recognition

We present in this paper a signal subspace-based approach for enhancing a noisy signal. This algorithm is based on a principal component analysis (PCA) in which the optimal sub-space selection is provided by a variance of the reconstruction error (VRE) criterion. This choice overcomes many limitations encountered with other selection criteria, like over-estimation 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, measures improvements in the distributed speech recognition of noisy signals corrupted by different types of additive noises. Our algorithm succeeds in improving the recognition of noisy speech in all noisy conditions.