THE INFINITE GAUSSIAN MODELS: AN APPLICATION TO SPEAKER IDENTIFICATION
When modeling speech with traditional Gaussian Mixture Models (GMM) a major problem is that one need to fix a priori the
number of GMMs. Using the infinite version of GMMs allows to overcome this problem. This is based on considering a
Dirichlet process with a Bayesian inference via Gibbs sampling rather than the traditional EM inference. The paper
investigates the usefulness of the infinite Gaussian modeling using the state of the art SVM classifiers. We consider the
particular case of the speaker identification under limited data condition that is very short speech sequences. Basically,
recognition rates of 100% are achieved after only 5 iterations using training and test samples less than 1 second. Experiments
are carried out over NIST SRE 2000 corpus.
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