MLP refined posterior features for noise robust phoneme recognition

Abstract

In this research, we have investigated improvement in the accuracy and robustness of phoneme recognition by refining posterior features extracted from single stream cepstral features. The refinement process is done using Multi Layer Perceptron (MLP) in a cascaded structure. The combination of frame posterior feature vectors, along with the entropy of each frame, as a confidence measure of posterior vectors, in the context window, is used to train a refiner MLP for estimating a new phoneme posterior feature set with the advantage of more accuracy and robustness. The confidence measure, as an informative feature, would enhance the refiner MLP performance in the correction of misclassified frames. The refiner MLP also models language level phonetics and lexical knowledge, using embedded information in the phoneme posteriors of a large context window. The suitability of these refined posterior features is evaluated on the tandem connectionist structures. Results show a significant improvement in both frame classification and phoneme recognition rates on the TIMIT acoustic and phonetic corpus compared to standard posterior features.