Speaker verification system has been currently recognized as an efficient security facility due to its cheapness and convenient usability. This system has to achieve fast enrollment and verification in order to make a willing acceptance to users, as well as low error rate. For accomplishing such low error rate, multilayer perceptrons (MLPs) are expected to be a good recognition method among various pattern recognition methods for speaker verification. MLPs process speaker verifications in modest speed even with a low-capable hardware because they share their internal weights between all recognizing models. On the other hand, considerable speaker enrolling delay is made mainly due to the large population of background speakers for low verification error, since the increasing number of the background speakers prolongs the learning times of MLPs. To solve this problem, this paper proposes an approach to reduce the number of background speakers needed to learn MLPs by selecting only the background speakers nearby to an enrolling speaker. An experiment is conducted using an MLP-based speaker verification system and Korean speech database. The result of the experiment shows efficient improvement of 23.5% in speaker enrolling time.
展开▼
