MLLR Transforms Based Speaker Recognition in Broadcast Streams

摘要

This paper deals with utilization of maximum likelihood linear regression (MLLR) adaptation transforms for speaker recognition in broadcast news streams. This task is specific particularly for widely varying acoustic conditions, microphones, transmission channels, background noise and short duration of recordings (usually in the range from 5 to 15 seconds). MLLR transforms based features are modeled using support vector machines (SVM). Obtained results are compared with a GMM based system with traditional MFCC features. The paper also deals with inter-session variability compensation techniques suitable for both systems and emphases the importance of feature vector scaling for SVM based system.