Noisy speech enhancement based on prior knowledge about spectral envelope and harmonic structure

Abstract

This paper considers the enhancement of noisy speech. Earlier studies have revealed that an approach that enhances spectral envelopes by using prior knowledge about the all-pole (AP) model parameters of clean speech learnt from speech corpora is advantageous in terms of the amount of musical noise and speech distortion. This paper proposes a new speech enhancement method, in which harmonic structure enhancement is incorporated in learning-based spectral envelope enhancement to further improve performance. The harmonic structure is represented by using a harmonic Gaussian mixture model (GMM), which is parameterized by a voicing indicator and a fundamental frequency. The parameters of the AP model and the harmonic GMM are jointly estimated by maximum a posteriori estimation, thus enabling the enhancement of spectral envelopes and harmonic structures in a unified framework. The proposed method outperforms the spectral envelope enhancement approach by 0.85 dB in cepstral distance.