Summary: A novel signal model consisting of a weighted sum of complex amplitude modulated signals is proposed as a suitable representation for non-stationary signals like speech. The estimation of model parameters is carried out by utilizing the accumulated autocorrelation functions of the modelled signal. The developed model is first fitted on noise-corrupted synthesized data, and then on sampled voiced speech data. The study demonstrates the suitability of the model.