Experiments with an extended adaptive SVD enhancement scheme for speech recognition in noise

An extension to adaptive signal subspace methods is presented, based on singular value decomposition (SVD) with an online estimation of the noise variance. With this approach aiming at automatic speech recognition (ASR) in adverse environmental conditions no speech detection has to be performed. A comparison of different SVD approaches and nonlinear spectral subtraction within ASR experiments of different applications is conducted for weakly correlated noise scenarios. Better performance in the case of signal subspace speech enhancement with respect to both accuracy as well as robustness of parameter tuning are reported