Discriminative feature extraction for speech recognition
Alain Biem, Shigeru Katagiri, B.-H. Juang · 2002
A novel approach to pattern recognition, called discriminative feature extraction (DFE) is introduced as a way to interactively handle the input data with a given classifier. The entire recognizer, consisting of the feature extractor as well as the classifier, is trained with the minimum classification error generalised probabilistic descent learning algorithm. Both the philosophy and implementation examples of this approach are described. DFE realizes a significant departure from conventional approaches, providing a comprehensive base for the entire system design. By way of example, an automatic scaling process is described, and experimental results for designing a cepstrum representation for vowel recognition are presented.>