A comparative evaluation of variance flooring techniques in HMM-based speaker verification
Haakan Melin, Johan W. Koolwaaij, Johan Lindberg, Frédéric Bimbot · 1998
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. 1. INTRODUCTION In practical applications, Automatic Speaker Verification (ASV) systems are generally used in contexts where very few client enrollment data are available. One problem with using small training data is the risk of over-training, that is, parameters of the client model are over-fitted to the particular training data. Especially var...