A Combination of Pre-Trained Approaches and Generic Methods for an Improved Speech Enhancement

To improve the quality of single-channel speech enhancement algorithms, various approaches include additional prior knowledge about speech, e.g., in the form of pre-trained speech models. In this paper, we consider a vector Taylor series based approach with a low-rank speech model. While employing a low-rank speech model keeps the complexity feasible, only speech spectral envelopes are represented and noise reduction between spectral harmonics is not possible. To counteract this issue, we propose a combination of generic, single-channel enhancement methods and the pre-trained vector Taylor series approach. Compared to a competing harmonic post-filter approach, the proposed combination is derived within a statistical framework and yields a better quality for the enhanced signal. This is verified using instrumental quality measures.