A discriminative neural prediction system for speech recognition

The authors propose a continuous speaker independent speech recognition system based on predictive neural networks for modelizing phonemes, and dynamic time warping for temporal alignment. In this system several modules cooperate, and this allows incorporation of a grammar model and simple correction rules. The neural networks are trained by using a frame discriminative criterion. Tests on the TIMIT database show 74.5% correct classification and 68.6% accuracy, which compares well with current systems (the CMU SPHINX System and the Cambridge Recurrent Error Propagation network).<>