A new Dynamic Synapse Neural Network for speech recognition

A new version of Dynamic Synapses Neural Network (DSNN) has been applied to recognize noisy raw waveforms of words spoken by multiple speakers. The new architecture of DSNN is based on the original DSNN and a wavelet fitter bank, which decomposes speech signals in multi-resolution frequency bands. In this study we have applied a Genetic Algorithm (GA) learning method to optimize the neural network. The advantage of the GA method is that it facilitates finding of a semi-optimal parameter set in the search space domain. In order to speed training time of the network, a new discrete time implementation of the DSNN has been introduced bused on the impulse invariant transformation, The network has been tested for difficult discrimination conditions.