Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005. / The Gaussian mixture model (GMM) performs very effectively in applications
such as speech and speaker recognition. However, evaluation speed is greatly
reduced when the GMM has a large number of mixture components. Various
techniques improve the evaluation speed by reducing the number of required
Gaussian evaluations.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/1639 |
| Date | 12 1900 |
| Creators | Cilliers, Francois Dirk |
| Contributors | Du Preez, J. A., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1024437 bytes, application/pdf |
| Rights | University of Stellenbosch |
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