Predictive connectionist approach to speech recognition

Abstract

This tutorial describes a context-dependent Hidden Control Neural Network (HCNN) architecture for large vocabulary continuous speech recognition. Its basic building element, the context-dependent HCNN model, is connectionist network trained to capture dynamics of sub-word units of speech. The described HCNN model belongs to a family of Hidden Markov Model/Multi-Layer Perceptron (HMM/MLP) hybrids, usually referred to as Predictive Neural Networks [1]. The model is trained to generate continuous real-valued output vector predictions as opposed to estimate maximum a posteriori probabilities (MAP) when performing pattern classification. Explicit context-dependent modeling is introduced to refine the baseline HCNN model for continuous speech recognition. The extended HCNN system was initially evaluated on the Conference Registration Database of CMU. On the same task, the HCNN modeling yielded better generalization performance than the Linked Predictive Neural Networks (LPNN). Additionally, several optimizations were possible when implementing the HCNN system. The tutorial concludes with the discussion of future research in the area of predictive connectionist approach to speech recognition. © Springer-Verlag Berlin Heidelberg 2005.