Maximum likelihood methods in vowel recognition: a comparative study

Vowel classification accuracy is studied using a generalized maximum likelihood ratio method. It is shown that two simplifying assumptions can reduce computation times by as much as a factor of five while producing practically no change in recognition accuracy. The two simplifying assumptions remove cross correlation terms and produce an Euclidean distance discriminant function. The vowels are taken from 350 multisyllabic isolated words spoken by five male speakers. The vowels occur in a variety of preand postconsonantal contexts. The recognition scores obtained for vowels are 83 percent. The effect of grouping of similar vowels on recognition scores is found to be marginal. The high back and high front vowels show better recognition scores (92-94 percent). In general, recognition performance for individual vowels follows a definite trend with respect to. the vowel diagram. A reasonable similarity is observed between confusion matrix and the distribution of vowels in first and second formant frequency (F1 F2) plane.