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While most speech enhancement algorithms improve speech quality, they may not improve speech intelligibility in noise. This paper focuses on the development of an algorithm that can be optimized for a specific acoustic environment and improve speech intelligibility. The proposed method decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target signal or the noise masker. Target-dominated T-F units are retained while masker-dominated T-F units are discarded. The Bayesian classifier is trained for each acoustic environment using an incremental approach that continuously updates the model parameters as more data become available. Listening experiments were conducted to assess the intelligibility of speech synthesized using the incrementally adapted models as a function of the number of training sentences. Results indicated substantial improvements in intelligibility (over 60% in babble at -5 dB SNR) with as few as ten training sentences in babble and at least 80 sentences in other noisy conditions.