Regular Section
Speech Reconstruction from MFCC Based on Nonnegative and Sparse Priors
2015 Volume E98.A Issue 7 Pages 1540-1543
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2015 Volume E98.A Issue 7 Pages 1540-1543
In this letter, high quality speech reconstruction approaches from Mel-frequency cepstral coefficients (MFCC) are presented. Taking into account of the nonnegative and sparse properties of the speech power spectrum, an alternating direction method of multipliers (ADMM) based nonnegative l2 norm (NL2) and weighted nonnegative l2 norm (NWL2) minimization approach is proposed to cope with the under-determined nature of the reconstruction problem. The phase spectrum is recovered by the well-known LSE-ISTFTM algorithm. Experimental results demonstrate that the NL2 and NWL2 approach substantially achieves better quality for reconstructed speech than the conventional l2 norm minimization approach, it sounds very close to the original speech when using the high-resolution MFCC, the PESQ score reaches 4.0.
