Automatic speaker recognition with Multi-resolution Gaussian Mixture models (MR-GMMs)
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| Main Author: | |
|---|---|
| Publication Date: | 2009 |
| Other Authors: | , , |
| Format: | Article |
| Language: | eng |
| Source: | Repositório Institucional da UnB |
| Download full: | http://repositorio.unb.br/handle/10482/11091 https://dx.doi.org/10.5769/J200901001 |
Summary: | 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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