Assessment of signal subspace based speech enhancement for noise robust speech recognition

Subspace filtering is an extensively studied technique that has been proven very effective in the area of speech enhancement to improve the speech intelligibility. In this paper, we review different subspace estimation techniques (minimum variance, least squares, singular value adaptation, time domain constrained and spectral domain constrained) in a modified singular value decomposition (SVD) framework, and investigate their capability to improve the noise robustness of speech recognisers. An extensive set of recognition experiments with the Resource Management (RM) database showed that significant reductions in WER can be obtained, both for the white noise and the coloured noise case. Unlike for speech enhancement approaches, we found that no truncation of the noisy signal subspace should be done to optimise the recognition accuracy.