Constrained MLLR for Speaker Recognition

One particularly difficult challenge for cross-channel MLLR (CMLLR) are two widely-used techniques for speaker introduced in the 2005 and 2006 NIST Speaker Recognition Evaluations, where training uses telephone speech and verification uses speech from multiple auxiliary comparable to that obtained with cepstral features. This paper describes a new feature extraction technique for speaker recognition based on CMLLR speaker adaptation which session effects through latent factor analysis (LFA) and through support vector machines (SVM). Results on the NIST operates directly on the recorded signal with noise well as in combination with two cepstral approaches such as reduction in the performance gap between telephone and auxiliary microphone data.