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Speech and other natural sounds show high temporal correlation and smooth spectral evolution punctuated by a few, irregular and abrupt changes. We model successive spectra as transformations of their immediate predecessors, capturing the evolution of the signal energy through time. The speech production model is used to decomposed the log-spectrogrum into two additive layers, which are able to separately explain and model the evolution of the harmonic excitation, and formant filtering of speech and similar sounds. We present results on a speech recognition task, that suggest that the model discovers a global structure on the dynamics of the signal's energy that helps to alleviate the problems generated by noise interferences. The model is also used to segment mixtures of speech into dominant speaker regions on a unsupervised source separation task.