Comparison of Gaussian and neural network classifiers on vowel recognition using the discrete cosine transform

The results of some experiments using a discrete cosine transform (DCT) to represent vowel spectra for classification by a neural network are described. The results are compared to a Gaussian classifier trained on the same database. The results show that the DCT classifies vowels using fewer coefficients than the cepstrum. The neural network classifier performs better than the Gaussian classifier, especially with large input feature sets consisting of delta coefficients and formant/pitch features. Best performance using these features was 58.2%. This compares well with other results reported for these data.<<ETX>>