Text independent voiceprint recognition model based on I-vector

The commonly used text independent Voiceprint recognition models are Gaussian Mixture Model (GMM) and GMM and general background model (GMM-UBM). In the equalization vector of the GMM model, both the speaker information and the channel information are included, which results in unstable performance of the recognition system of the GMM and GMM-UBM models. In addition, the recognition ability for cross channel is poor, moreover, both models are limited by the maximum likelihood criterion. So, they employ weak ability to distinguish categories. I-vector is also known as identity authentication vector and has been proposed on the basis of Gaussian super vector in recent years. The method uses one space instead of the two spaces, including the difference between the speakers and the difference between the channels, and it is known as the most cutting-edge speaker modeling technology available today. Therefore, this paper adopted i-vector framework as the speaker recognition model, and studied the main problems that need to be dealt with. The recognition effect of GMM-UBM model and i-vector model were investigated by experiment as well. Through comparison experiments, it is verified that the i-vector recognition model employs a lower error rate of the and is more efficient. In the recognition phase, to quickly recognize the speaker's identity only needs to record two seconds of speech, and the system recognition accuracy reaches 97%.