On-line incremental adaptation for speaker verification using maximum likelihood estimates of CDHMM parameters

Investigates two approaches to the online incremental adaptation of continuous-density hidden Markov model (CDHMM) parameters. First, the popular maximum a-posteriori (MAP) approach is examined, highlighting difficulties in automatically setting the adaptation rate. To overcome these problems, we introduce a new approach, based on the multi-observation estimation equations of the forward-backward algorithm, called the cumulative likelihood estimate (CLE). Experimental results using these two approaches are compared with and without the use of a speech model for enrolment on isolated-word speaker models. In both enrolment procedures, the CLE approach can achieve an equal error rate (EER) of approximately 1% for six adaptation sequences using a single-digit test token