Sparse representation-based quasi-clean speech construction for speech quality assessment under complex environments

作者：Zhou, Weili; He, Qianhua^*; Wang, Yalou; Li, Yanxiong

来源：IET Signal Processing, 2017, 11(4): 486-493.

DOI：10.1049/iet-spr.2016.0555

• speech processing
• signal representation
• signal reconstruction
• signal denoising
• singular value decomposition
• correlation methods
• iterative methods
• sparse representation-based quasiclean speech construction
• complex environments
• nonintrusive speech quality assessment method
• sparse representation-based speech reconstruction algorithm
• noisy degraded signal
• over-complete dictionary
• clean speech power spectrum
• K-singular value decomposition algorithm
• stopping residue error

A non-intrusive speech quality assessment method for complex environments was proposed. In the proposed approach, a new sparse representation-based speech reconstruction algorithm was presented to acquire the quasi-clean speech from the noisy degraded signal. Firstly, an over-complete dictionary of the clean speech power spectrum was learned by the K-singular value decomposition algorithm. Then in the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the noise spectrum which was adjusted by a posteriori SNR-weighted factor, and the orthogonal matching pursuit approach was applied to reconstruct the clean speech spectrum from the noisy speech. The quasi-clean speech was considered as the reference to a modified PESQ perceptual model, and the mean opinion score of the noisy degraded speech was achieved via the distortions estimation between the quasi-clean speech and the degraded speech. Experimental results show that the proposed approach obtains a correlation coefficient of 0.925 on NOIZEUS complex environment database, which is 99% similar to the performance of the intrusive standard ITU-T PESQ, and 7.1% outperforms non-intrusive standard ITU-T P.563.

• 出版日期2017-6
• 单位华南理工大学