Sparse representation‐based quasi‐clean speech construction for speech quality assessment under complex environments
Weili Zhou, Qianhua He, Yalou Wang, Yanxiong Li · IET Signal Processing · 2017
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.