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In this paper a time-frequency estimator for enhancement of noisy speech signals in the DFT domain is introduced. This estimator is based on modelling and filtering the temporal trajectories of the DFT components of noisy speech signal using Kalman filters. The time-varying trajectory of the DFT components of speech is modelled by a low order autoregressive process incorporated in the state equation of Kalman filter. The performance of the proposed method for the enhancement of noisy speech is evaluated and compared with MMSE log-STSA estimator and parametric spectral subtraction. Evaluation results show that the incorporation of temporal information through Kalman filters results in reduced residual noise and improved perceived quality of speech.