Improved speaker model migration via stochastic synthesis [speaker recognition applications]

Model migration in speaker recognition is a task of converting parametrically-obsolete models to new structures and configurations without the requirement to store the original speech waveforms or feature vector sequences along with the models. This paper reports on further enhancements of a statistical migration technique based on Gaussian mixture models, introduced previously. The present approach is based on a stochastic synthesis of feature sequences from obsolete models that are subsequently used to create the new models. Here, in addition to Gaussian means and priors, as utilized in the previous contribution, also the covariances are included resulting in significant performance gains in the migrated models, compared to the mean-only method. Overall, measured on the NIST 2003 cellular task, the described algorithm achieves a model migration incurring a loss in performance of 8-20% relative to a full re-enrollment from waveforms, dependent on the type of mismatch between the obsolete and the new configuration. The inclusion of the covariance information is shown to reduce the loss of performance by a factor of 3-4 as compared to the baseline mean-only migration technique.