Speaker Authentication Using GMM-UBM

Today, numerous biometric systems have been suggested and created. Most used among them are face, fingerprint, and voice recognition. Each of them has their own advantages and disadvantages. Discussing about the automatic speaker verification (ASV), speech signal is a natural signal which one can easily obtain even from a telephone call. Research in this area is carried out from decades. This speaker verification technology has advanced in recent years and has become a genuine method for biometric systems. Using an adaptable Gaussian Mixture Model (GMM-UBM), a text-independent speaker verification (TISV) technique has been developed and compared with state-of-art I-vector based speaker recognition system in this research. Parameters for universal background model (UBM) are trained using the EM (Expectation maximization) and MAP adaptation method is used for training speaker models. Multiple false acceptance and false rejection rates are calculated by changing the threshold values for comparison. The results are shown in equal error rate (EER) for both the GMM-UBM, and I-vector based ASV systems, and the lowest EER is found to be 5.31% and 4.74%, respectively, after adjusting the threshold settings and number of Gaussian mixtures utilized.