Isolated digit recognition using a block diagonal recurrent neural network

The objective of this paper is to recognize speech based on speech prediction techniques using a discrete time recurrent neural network (DTRNN) with a block diagonal feedback weight matrix called the block diagonal recurrent neural network (BDRNN). The ability of this network has been investigated for the TIMIT isolated digits spoken by a representative speaker. Simulation results for classifying the utterances show that the size of the BDRNN required is very small compared to multilayer perceptron networks with time delayed feedforward connections.