Speaker Recognition Using Gaussian Mixture Model
Satyendra Nath Mandal, Abhranil Chatterjee, Debayan Das · SSRN Electronic Journal · 2014
Speaker Recognition is the computing task of recognizing a speaker using some speaker dependent characteristic of his/her voice, known as feature. The recognition process consists of three main stages: feature extraction, speaker modeling and speaker pruning or decision making. In this paper, numerical features of each speaker have been reduced using k-means clustering. The result of k-means clustering is improved by applying Gaussian Mixture Model to reduce the time complexity. The feature of the unknown speaker is compared with that of the speakers stored in the codebook. The speaker is identified from the codebook based on the maximum probability using likelihood function. It is observed that the accuracy of speaker recognition can be improved using this approach.