The Approach of Speaker Diarization by Gaussian Mixture Model (GMM)

Speaker identification is an important activity in the process of speaker diarization. We need to model the speaker by Gaussian mixture model (GMM) for speaker identification purpose. Large GMM is called as a Universal Background Model (UBM) which is adapted into each speaker model for speaker identification purpose. This paper focuses on speech clustering for speaker diarization. The speaker diarization includes the steps speech segmentation and the process of speech clustering. In speech segmentation, the features are extracted for each speech segment which is converted into Mel-FrequencyCepstralCoefficients (MFCC). Each speech segment is modeled by UBM adaptation. The relevant speech segments are grouped as speech clusters. This paper describes the speech segmentation, UBM adaptation, and speech clustering technique.