Automatic Enrollment for Spea

Enrollment is a necessary session in speaker verification. Automatic verification of collected training utterances, so called automatic enrollment, is critical to the performance of any speaker verification system. In this paper, we propose one solution including two separate approaches for automatic enrollment. First, at the utterance level, we propose to use an N-best algorithm to perform spoken content verification for training utterance selection. Second, at the word level, we employ an accurate utterance verification algorithm to conduct word verification for training data selection. Finally, we combine the two techniques together and propose a solution for automatic enrollment. Our experiments show that the N-best approach and the utterance verification approach can provide very low error rates. Based on the experimental results, we expect that the combined solution is close to error free and is feasible for real-world applications.