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In this paper, we describe a tuning method based on a robustness metric and extended to work with the augmented Kalman filter for enhancing coloured-noise-corrupted speech. The method proposed within utilises the robustness metric to provide dynamic and adaptive tuning of the Kalman filter gain in order to reduce the residual noise that results from poor speech model estimates. An analysis of the Kalman filter recursion equations is presented that augments the robustness metric equations to include coloured noise model parameters. Objective and blind AB subjective listening tests were performed on the NOIZEUS speech corpus for both white and coloured noises with the results being compared with the MMSE method. In the blind AB subjective testing, the 15 English-speaking listeners showed preference for the proposed method over both the MMSE and oracle Kalman filter methods (where clean speech parameters were used). These results imply that the proposed tuned Kalman filter produces more perceptibly-acceptable enhanced speech than the oracle Kalman filter, which is considered the ideal for this enhancement technique.

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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. 