EUROSPEECH 2003 - INTERSPEECH 2003
The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing weights, we believe that there exists a better means of combining individual Gaussian mixture models. The proposed AdaBoost-GMM method is non-parametric in which a selected set of weak classifiers, each constructed based on a single Gaussian model, is optimally combined to form a strong classifier, the optimality being in the sense of maximum margin. Experiments show that the boosted GMM classifier yields 10.81% relative reduction in equal error rate for the same handsets and 11.24% for different handsets, a significant improvement over the baseline adapted GMM system.
Bibliographic reference. Li, Stan Z. / Zhang, Dong / Ma, Chengyuan / Shum, Heung-Yeung / Chang, Eric (2003): "Learning to boost GMM based speaker verification", In EUROSPEECH-2003, 1677-1680.