In this paper, we present a non-iterative Kalman filtering algorithm that applies a dynamic adjustment factor on the Kalman filter gain to alleviate the negative effects of estimating speech model parameters from noise-corrupted speech. These poor estimates introduce a bias in the first component of the Kalman gain vector, particularly during the silent (non-speech) regions, resulting in a significant level of residual noise in the enhanced speech. The proposed dynamic gain adjustment algorithm utilises a recently developed metric for quantifying the level of robustness in the Kalman filter. Objective and human subjective listening tests on the NOIZEUS speech database were performed. The results showed that the output speech from the proposed algorithm has improved quality over the noniterative Kalman filter that uses noisy model estimates and is competitive with the MMSE-STSA method.