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A computationally efficient, high quality vector quantization scheme based on a parametric probability density function (PDF) is proposed. In this scheme, the observations are modeled as i.i.d realizations of a multivariate mixture density. The mixture model parameters are efficiently estimated using the expectation maximization (EM) algorithm. The estimated density is suitably quantized using transform coding and bit allocation techniques for both fixed rate and variable rate systems. The usefulness of the approach is demonstrated for speech coding where Gaussian mixture models are used to model speech line spectral frequencies. An attractive feature of this method is that source encoding using the resultant codebook involves no searches and its computational complexity is minimal and independent of the rate of the system. Furthermore, the proposed scheme is scalable and can switch between memoryless quantizer and quantizer with memory seamlessly. The quantizer with memory is shown to provide transparent quality speech at 16 bits/frame.