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In this paper we demonstrate that spectral conversion can be successfully applied to the speech enhancement problem as a feature denoising method. The enhanced spectral features can be used in the context of the Kalman filter for estimating the clean speech signal. In essence, instead of estimating the clean speech features and the clean speech signal using the iterative Kalman filter, we show that is more efficient to initially estimate the clean speech features from the noisy speech features using spectral conversion (using a training speech corpus) and then apply the standard Kalman filter. Our results show an average improvement compared to the iterative Kalman filter that can reach 6 dB in the average segmental output Signal-to-Noise Ratio (SNR), in low input SNR's.
Spectral conversion, denoising, speech enhancement, spectral features, Kalman Filter
Athanasios Mouchtaris, Jan Van der Spiegel, Paul Mueller, and P. Tsakalides, "A Spectral Conversion Approach to Feature Denoising and Speech Enhancement", . September 2005.
Date Posted: 14 November 2005
This document has been peer reviewed.