Isolated vowel recognition using linear predictive features and neural network classifier fusion

Abstract

In this work, various linear predictive feature vectors were used to train three different automated neural networks type classifiers for the task of isolated vowel recognition. The features used included linear prediction filter coefficients, reflection coefficients, log area ratios, and the linear predictive cepstrum. The three neural network classifiers used are the multilayer perceptron, radial basis function and the probabilistic neural network. The linear predictive cepstrum of dimension 12 is the best feature especially when training is done on clean speech and testing is done on noisy speech. Three different classifier fusion strategies (linear fusion, majority voting and weighted majority voting) were found to improve the performance. Linear fusion with varying weights is the best method and is most robust to noise.