Speech Recognition Using Neural Networks
Joe Tebelskis · 2015
This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system.Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling.Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness.Neural networks avoid many of these assumptions, while they can also learn complex functions, generalize effectively, tolerate noise, and support parallelism.While neural networks can readily be applied to acoustic modeling, it is not yet clear how they can be used for temporal modeling.Therefore, we explore a class of systems called NN-HMM hybrids, in which neural networks perform acoustic modeling, and HMMs perform temporal modeling.We argue that a NN-HMM hybrid has several theoretical advantages over a pure HMM system, including better acoustic modeling accuracy, better context sensitivity, more natural discrimination, and a more economical use of parameters.These advantages are confirmed experimentally by a NN-HMM hybrid that we developed, based on context-independent phoneme models, that achieved 90.5% word accuracy on the Resource Management database, in contrast to only 86.0% accuracy achieved by a pure HMM under similar conditions.I wish to thank Alex Waibel for the guidance, encouragement, and friendship that he managed to extend to me during our six years of collaboration over all those inconvenient oceans -and for his unflagging efforts to provide a world-class, international research environment, which made this thesis possible.Alex's scientific integrity, humane idealism, good cheer, and great ambition have earned him my respect, plus a standing invitation to dinner whenever he next passes through my corner of the world.