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Prior knowledge of familiar auditory patterns is essential for separating sound sources in human auditory processing. Speech recognition modeling is one probabilistic way for capturing these familiar auditory patterns. In this paper we focus on separating speech sources with a single-microphone input only. A model-based algorithm is proposed to generate target speech by estimating its spectral envelope trajectory and filtering irrelevant harmonic structure of the interference. The spectral trajectory is optimally regenerated in the form of line spectrum pair (LSP) parameters. Experiments on separating mixed speech sources are presented. Objective evaluation shows that interference is significantly reduced and the output speech is highly intelligible and sounds fairly clear.