Compressive sensing framework for speech signal synthesis using a hybrid dictionary

Compressive sensing (CS) is a promising focus in signal processing field, which offers a novel view of simultaneous compression and sampling. In this framework a sparse approximated signal is obtained with samples much less than that required by the Nyquist sampling theorem if the signal is sparse on one basis. Encouraged by its exciting potential application in signal compression, we use CS framework for speech synthesis problems. The linear prediction coding (LPC) is an efficient tool for speech compression, as the speech is considered to be an AR process. It is also known that a speech signal is quasi-periodic in its voiced parts, hence a discrete fourier transform (DFT) basis will provide a better approximation. Thus we propose a hybrid dictionary combined with the LPC model and the DFT model as the basis of speech signal. The orthogonal matching pursuit (OMP) is employed in our simulations to compute the sparse representation in the hybrid dictionary domain. The results indicate good performance with our proposed scheme, offering a satisfactory perceptual quality.