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A joint source-channel decoding scheme that improves the performance of conventional channel decoders over erasure channels by exploiting the cross-correlation between successive speech frames is presented. Speech spectrum parameters are quantized using the scheme presented in Subramaniam and Rao (2001). The joint probability density function (PDF) of the spectrum parameters of successive speech frames is modelled using a Gaussian mixture model (GMM). This model is then used to process the channel decoder output over erasure channels. The performance of two decoding strategies, namely, maximum likelihood decoding (ML) and minimum mean squared error decoding (MMSE) is shown to provide significantly better performance than prediction based schemes.