Text-independent speaker identification using GMM-UBM and frame level likelihood normalization

In this paper, we describe a Gaussian mixture model-universal background model (GMM-UBM) speaker identification system. In this GMM-UBM system, we derive the hypothesized speaker model by adapting the parameters of UBM using the speaker's training speech and a form of Bayesian adaptation. The UBM technique is incorporated into the GMM speaker identification system to reduce the time requirement for recognition significantly. The paper also presents a new frame level likelihood score normalization for adjusting different scores of speaker models to get more robust scores in the final decision. Experiments on the 2000 NIST speaker recognition evaluation corpus show that GMM-UBM and frame level likelihood score normalization yield better performance. Compared to the baseline system, around 31.2% relative error reduction is obtained from the combination of both techniques.