Discrete utterance recognition based on nonlinear model identification with single layer neural networks

A scheme for speaker independent discrete utterance recognition using single layer neural network (SLNN) based nonlinear auto regression model parameters as the features is presented. A fast training algorithm is developed for the identification of the model parameters. Dynamic programming is used for the pattern matching. This study demonstrates that the SLNN can be used successfully as the short time nonlinear auto regression model of the speech signal and thus acts as a feature extractor for speech recognition. Ten digits uttered by twelve speakers were used as the database to examine the performance of the SLNN based feature extractor of speech as compared to the standard linear prediction technique.<>