Name
Title | ||
|---|---|---|
Speaker Verification Based on Different Vector Quantization Techniques with Gaussian Mixture Models |
Abstract | ||
|---|---|---|
The introduction of Gaussian Mixture Models (GMMs) in the field of speaker verification has led to very good results. This paper illustrates an evolution in state-of-the-art Speaker Verification by highlighting the contribution of recently established information theoretic based vector quantization technique. We explore the novel application of three different vector quantization algorithms, namely K-means, Linde-Buzo-Gray (LBG) and Information Theoretic Vector Quantization (ITVQ) for efficient speaker verification. The Expectation Maximization (EM) algorithm used by GMM requires a prohibitive amount of iterations to converge. In this paper, comparable alternatives to EM including K-means, LBG and ITVQ algorithm were tested. The GMM-ITVQ algorithm was found to be the most efficient alternative for the GMM-EM. It gives correct classification rates at a similar level to that of GMM-EM. Finally, representative performance benchmarks and system behaviour experiments on NIST SRE corpora are presented. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NSS.2009.19 | Gold Coast, QLD |
Keywords | Field | DocType |
information theoretic vector quantization,itvq algorithm,different vector quantization algorithm,gmm-itvq algorithm,different vector quantization techniques,speaker verification,gaussian mixture models,vector quantization technique,efficient alternative,efficient speaker verification,expectation maximization,expectation maximization algorithm,speech,mel frequency cepstral coefficient,k means,gaussian processes,classification algorithms,gaussian mixture model,feature extraction,em algorithm,speaker recognition,vector quantization,algorithm design and analysis | Algorithm design,Pattern recognition,Linde–Buzo–Gray algorithm,Expectation–maximization algorithm,Computer science,Learning vector quantization,Speaker recognition,Vector quantization,Gaussian process,Artificial intelligence,Mixture model | Conference |
ISBN | Citations | PageRank |
978-0-7695-3838-9 | 7 | 0.62 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheeraz Memon | 1 | 21 | 2.88 |
| Margaret Lech | 2 | 239 | 24.84 |
| Namunu C. Maddage | 3 | 345 | 26.51 |