Speaker Verification System Using Gaussian Mixture Model & UBM

In This paper presents an overview of a stateof-the-art text-independent speaker verification system. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Gaussian mixture modeling, which is the speaker modeling Technique used in most systems, is then explained. This project introduces and motivates the use of Gaussian mixture model (GMM) for text-independent speaker recognition. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modelling speaker identity. The present work Background Model (GMM– UBM) is also made. Keywordsspeaker recognition; Gaussian mixture models; likelihood ratio detector; universal background model;