A comperative evaluation of variance flooring techniques on HMM-based speaker verification

Abstract

The problem of how to train variance parameters on scarce data is addressed in the context of text-dependent, HMM-based, automatic speaker verification. Three variations of variance flooring is explored as a means to prevent over-fitting. With the best performing one, the floor to a variance vector of a client model is proportional to the corresponding variance vector in a non-client multi-speaker model. It is also found that adapting the means and mixture weights from the non-client model while keeping variances constant works comparably to variance flooring and is much simpler. Comparisons are made on three large telephone quality corpora.