8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


Learning to Boost GMM Based Speaker Verification

Stan Z. Li, Dong Zhang, Chengyuan Ma, Heung-Yeung Shum, Eric Chang

Microsoft Research Asia, China

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.

Full Paper

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.