Speaker identification via support vector classifiers

A novel approach to speaker identification is presented. The technique, based on Vapnik's (1995) work with support vectors, is exciting for several reasons. The support vector method is a discriminative approach, modeling the boundaries directly between speakers voices in some feature space rather than by the difficult intermediate step of estimating speaker densities. Most importantly, support vector discriminant classifiers are unique in that they separate training data while keeping discriminating power low, thereby reducing test errors. As a result it is possible to build useful classifiers with many more parameters than training points. Furthermore, Vapnik's theory suggests which class of discriminating functions should be used given the amount of training data by being able to determine bounds on the expected number of test errors. Support vector classifiers are efficient to compute compared to other discriminant functions. Though experimental results are preliminary, performance improvements over the BBN modified Gaussian Bayes decision system have been obtained on the Switchboard corpus.