Discriminative learning of additive noise and channel distortions for robust speech recognition
Learning the influence of additive noise and channel distortions from training data is an effective approach for robust speech recognition. Most of the previous methods are based on maximum likelihood estimation criterion. We propose a new method of discriminative learning environmental parameters, which is based on the minimum classification error (MCE) criterion. By using a simple classifier defined by ourselves and the generalized probabilistic descent (GPD) algorithm, we iteratively learn environmental parameters. After getting the parameters, we estimate the clean speech features from the observed speech features and then use the estimation of the clean speech features to train or test the back-end HMM classifier. The best error rate reduction of 32.1% is obtained, tested on a Korean 18 isolated confusion words task, relative to the conventional HMM system.