Characterization Vector Extraction Using Neural Network for Speaker Recognition

摘要

The State-of-the-art speaker recognition system is now using the i-vector framework to make the supervector of the UBM to a low-dimensional vector. In this paper, we propose a new method to do the same convert which contains more speaker's information. This method, using the mind of bottleneck, is based on the usual Artificial Neural Network. The low-dimensional vector extracted from the new method is more speaker-dependent and it is effective in interview microphone speech. Our experiment focus on the comparison between usual i-vectors and the new vectors we proposed. The results of our experiment indicate that the equal error rate and the minimum detection cost are improved by using our new method.