Optimized subspace weighting for robust speech recognition in additive noise environments
Kris Hermus, Werner Verhelst, Patrick Wambacq · 2000
Signal Subspace (SS) based speech enhancement techniques obtain significant additive-noise reduction by altering the singular value spectrum of the speech observation matrix. Among the class of different possible SS weighting strategies, the Minimum Variance (MV) estimation method substantially increases the speech recognition accuracy in additive noise environments, outperforming the widely used Spectral Subtraction methods. However, these SS approaches are developed as pure speech enhancement techniques, and it is still unknown how effective they are for noise robust speech recognition. In this respect, we present the idea of `optimal SS weighting' for speech recognition systems, and we illustrate in detail that the MV estimation closely approximates this optimum. We applied the SS weighting methods to a LV-CSR task with noisy data (10 dB SNR), and obtained relative reductions in Word Error Rate of more than 60%.