Analysis-synthesis based speech enhancement with improved spectrum envelope estimation by tracking speech dynamics

This paper presents a Kalman tracking approach to re-estimate clean spectral amplitude from noisy speech spectrum for re-synthesis based speech enhancement. The motivation of using Kalman filter and training is to exploit the temporal correlation between speech dynamics and to include prior knowledge of speech to improve the model parameter estimation in harmonic noise model (HNM) based speech enhancement system. The re-estimated harmonic amplitude is fitted into an analysis-synthesis framework to accomplish a more accurate HNM based re-synthesis. Objective evaluation results show the proposed method achieves significant improvement over various classical short-time spectral amplitude (STSA) based methods, especially in low signal-to-noise ratio environments.