Training algorithms for a predictive classifier
P. V. Rao, R. Raveendran · 2002
Some aspects of training methods in building a predictive classification model are studied in the context of discriminating between the stop consonants based on their places of articulation. Two training algorithms are taken-up: one from the likelihood training category and the other from the discriminative training category. These algorithms are discussed in a unified framework using information theoretic concepts to bring out their advantages and limitations with reference to the classification task. The experimental studies on recognition confirm the usefulness of exploiting these advantages for modelling the stop consonant transition region by a predictor. A waveform based scalar predictor, trained by the modified maximum likelihood (first) algorithm shows a substantial reduction in pitch pulse interference. The significance of discriminatively training the predictive classifier using a maximum mutual information type (second) algorithm is brought out in a spectral prediction framework.