Neural network comparison of speech recognition system using trispectrum analysis in noisy environment

In this paper, a speech recognition system is developed using higher order statistic (HOS) with its fourth order of crosscorrelation (trispectrum) analysis. To analysis the distribution of the trispectrum data along its two dimensional representation, we developed an adaptive feature extraction mechanism of the trispectrum speech data based on cascade neural network that consists of SOFM (Self-Organizing Feature Map) and LVQ (Learning Vector Quantization). This cascade neural network is used as an adaptive codebook generation algorithm for determining the feature distribution of the trispectrum speech data. Two types of neural networks, namely back-propagation neural network and probabilistic neural networks, are then used as the pattern classifier of this speech recognition system. Comparison of the recognition system using those neural networks as the classifier is conducted based on sample data with and without Gaussian noise. Experimental result shown that PNN has superior recognition rate compare with that of BPNN, especially when a harsh condition of noise is added to the system.