A neural net approach to speech recognition
William Y. C. Huang, Richard P. Lippmann, Ben Gold · 2003
Artificial neural networks are of interest because algorithms used in many speech recognizers can be implemented using highly parallel neural net architectures and because new parallel algorithms are being development that are inspired by biological nervous systems. Some neural net approaches are resented for the problem of static pattern classification and time alignment. For static pattern classification, multi-layer perceptron classifiers trained with back propagation can form arbitrary decision regions, are robust, and train rapidly for convex decision regions. For time alignment, the Viterbi net is a neural net implementation of the Viterbi decoder used very effectively in recognition systems based on hidden Markov models (HMMs).>