ADAPTATION TECHNIQUES FOR SPEAKER RECOGNITION
Costas Boulis · 2002
Several adaptation techniques are compared for the task of speaker recognition in the Switchboard database. Adaptation techniques have been proven to be successful for the task of speech recognition and a number of them are investigated here for their possible usefulness in the speaker recognition problem, since many of the issues are common in both tasks. Both transformation-based and approximate Bayesian approaches are used. The proposed techniques are compared with a baseline system of a mixture of Gaussians for each one of the target speakers. Results show that adaptation methods can outperform standard ML techniques.