SVM-GMM Based Speaker Identification

Abstract

Although a great success has been achieved under the environment of lab where the training data is sufficient and the surroundings are quiet, speaker identification (SI) in practical use still remains a challenge because of the complicated environment. To tackle this challenge, a hybrid system of Gaussian mixture model-support vector machines (GMM-SVM) is proposed in this paper. SVM can do well with less data but is computationally expensive while GMM is computationally inexpensive but needs more data to perform adequately. In this paper, SVM and GMM are parallel in both the training and testing phase, the judgment of them are fused to make the final decision: the person with the largest score is identified as the true speaker. Universal background model (UBM) is used in GMM to improve the recognition accuracy. The system is evaluated on part of the TIMIT database and a Chinese database which is recorded by our own. Experiments have shown that the method proposed in this paper is effective. The system has better performance and robustness than the baseline systems.