Classification of unvoiced stops based on formant transitions prior to release

A feature set that captures the dynamics of formant transitions is utilized to classify the unvoiced stop consonants. The second formant and its slope are used to characterize the transition between the vowel and the closure in a VCV (vowel-consonant-vowel) environment. The performance of a feature set obtained by means of a time-varying, closed-glottis model for the signal is compared with that of a standard LPC (linear predictive coding) model. The different feature sets are evaluated on a database consisting of eight speakers. A fourfold reduction in the error rate is obtained by means of the more sophisticated model. The performance of three different classifiers is presented. A novel adaptive algorithm, the learning vector classifier, is compared with standard K-means and LVQ2 (learning vector quantization-2) classifiers. Error rates of 5% are obtained for the three-way classification