EUROSPEECH '95

A vector quantisation codebook can be modelled as a set of probability density functions. The problem of estimating the parameters determining mixture probability density models can be solved using a loglikelihood based reestimation procedure. On the other hand, this problem can be also viewed as a conventional optimisation problem. Consequently, gradient descent techniques may be used to obtain values of the model parameters. The main advantage of these techniques over the reestimation procedure is higher robustness due to an initial estimation of the model parameters. In the paper, we describe a descent algorithm along with a criterion function, we propose. We obtained some promising results by applying this algorithm to one and twovariate pseudo Gaussian mixture probability density functions and further to signal vectors of a continuous speech database.
Bibliographic reference. Dobrisek, S. / Mihelic, R. / Pavesic, N. (1995): "Multivariate mixture probability density modelling of VQ codebook using gradient descent algorithm", In EUROSPEECH1995, 14311434.