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- Title
Speaker Recognition Using Gaussian Mixture Model.
- Authors
Mandal, Satyendra Nath; Chatterjee, Abhranil; Das, Debayan
- Abstract
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.
- Subjects
AUTOMATIC speech recognition equipment; GAUSSIAN mixture models; AUTOMATIC speech recognition; PROBABILITY theory; K-means clustering
- Publication
IUP Journal of Computer Sciences, 2014, Vol 8, Issue 2, p7
- ISSN
2583-441X
- Publication type
Article