Time-varying feature selection and classification of unvoiced stop consonants

A feature set that captures the dynamics of formant transitions prior to closure in a VCV environment is used to characterize and classify the unvoiced stop consonants. The feature set is derived from a time-varying, data-selective model for the speech signal. Its performance is compared with that of comparable formant data from a standard delta-LPC-based model. The different feature sets are evaluated on a database composed of eight talkers. A 40% reduction in classification error rate is obtained by means of the time-varying model. The performance of three different classifiers is discussed. A novel adaptive algorithm, termed learning vector classifier (LVC) is compared with standard K-means and LVQ2 classifiers. LVC is a supervised learning classifier that improves performance by increasing the resolution of the decision boundaries. Error rates obtained for the three-way (p, t, and k) classification task using LVC and the time-varying analysis are comparable to that of techniques that make use of additional discriminating information contained in the burst. Further improvements are expected when an expanded time-varying feature set is utilized, coupled with information from the burst