Robustness metric-based tuning of the augmented Kalman filter for the enhancement of speech corrupted with coloured noise

Publication date: Available online 11 October 2018Source: Speech CommunicationAuthor(s): Aidan E.W. George, Stephen So, Ratna Ghosh, Kuldip K. PaliwalAbstractIn 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.

Source: Speech Communication - October 11, 2018 Category: Speech-Language Pathology Source Type: research