Discriminative training for continuous speech recognition

Discriminative training techniques for Hidden-Markov Models were recently proposed and successfully applied for automatic speech recognition. In this paper a discussion of the Minimum Classification Error and the Maximum Mutual Information objective is presented. An extended reestimation formula is used for the HMM parameter update for both objective functions. The discriminative training methods were utilized in speaker independent phoneme recognition experiments and improved the phoneme recognition rates for both discriminative training techniques. (orig.)SIGLEAvailable from TIB Hannover: RR 5221(110)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

A hybrid RBF-HMM system for continuous speech recognition

A hybrid system for continuous speech recognition, consisting of a neural network with Radial Basis Functions and Hidden Markov Models is described in this paper together with discriminant training techniques. Initially the neural net is trained to approximate a-posteriori probabilities of single HMM states. These probabilities are used by the Viterbi algorithm to calculate the total scores for the individual hybrid phoneme models. The final training of the hybrid system is based on the 'Minimum Classification Error' objective function, which approximates the misclassification rate of the hybrid classifier, and the 'Generalized Probabilistic Descent' algorithm. The hybrid system was used in continuous speech recognition experiments with phoneme units and shows about 63.8% phoneme recognition rate in a speaker-independent task. (orig.)Available from TIB Hannover: RR 5221(109)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

A new model-discriminant training algorithm for hybrid NN-HMM systems

This paper describes a hybrid system for continuous speech recognition consisting of a neural network (NN) and a hidden Markov model (HMM). The system is based on a multilayer perceptron, which approximates the a-posteriori probability of a sequence of states, derived from semi-continuous hidden Markov models. The classification is based on a total score for each hybrid model, attained from a Viterbi search on the state probabilities. Due to the unintended discrimination between the states in each model, a new training algorithm for the hybrid neural networks is presented. The utilized error function approximates the misclassification rate of the hybrid system. The discriminance between the correct and the incorrect models is optimized during the training by the 'Generalized Probabilistic Descent Algorithm', resulting in a minimum classification error. No explicit target values for the neural net output nodes are used, as in the usual backpropagation algorithm with a quadratic error function. In basic experiments up to 56% recognition rate were achieved on a vowel classification task and up to 69% on a consonant cluster classification task. (orig.)SIGLEAvailable from TIB Hannover: RR 5221(108)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

Verbesserte Methoden zur Objektivierung von subjektiven Bewertungen des Fahrverhaltens

Available from TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman