Gaussian Mixture Models (GMMs) are the most widely used technique for voice modeling in automatic speaker recognition systems. In this paper, we introduce a variation of the traditional GMM approach that uses models with variable complexity (resolution). Termed Multi-resolution GMMs (MR-GMMs); this new approach yields more than a 50% reduction in the computational costs associated with proper speaker identification, as compared to the traditional GMM approach. We also explore the noise robustness of the new method by investigating MR-GMM performance under noisy audio conditions using a series of practical identification tests.
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THE INTERNATIONAL JOURNAL OF FORENSIC COMPUTER SCIENCE - IJoFCS
Volume 4, Number 1, pp 9-21, DOI: 10.5769/J200901001 or http://dx.doi.org/10.5769/J200901001
Automatic Speaker Recognition with Multi-Resolution Gaussian Mixture Models (MR-GMM)
By Frederico D’Almeida, Francisco Assis Nascimento, Pedro Berger, and Lúcio Silva
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