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This paper proposes a novel ICA-based MAP speech enhancement algorithm using multiple variable speech distribution models. The proposed algorithm consists of two stages, primary and advanced enhancement. The primary enhancement is performed by employing a single distribution model obtained from all speech signals. The advanced enhancement first employs multiple models of speech signals, each modeling a specific type of speech, and then adapts these model parameters for each speech frame by employing the enhanced signal from the primary estimation. A statistical noise adaptation technique has been employed to better model the noise in non-stationary case. The proposed algorithm has been evaluated on speech from TIMIT database corrupted by various noises and it has shown significantly improved performance over using the single speech distribution model.