Least 1-norm pole-zero modeling with sparse deconvolution for speech analysis

In this paper, we present a speech analysis method based on sparse pole-zero modeling of speech. Instead of using the all-pole model to approximate the speech production filter, a pole-zero model is used for the combined effect of the vocal tract; radiation at the lips and the glottal pulse shape. Moreover, to consider the spiky excitation form of the pulse train during voiced speech, the modeling parameters and sparse residuals are estimated in an iterative fashion using a least 1-norm pole-zero with sparse deconvolution algorithm. Compared with the conventional two-stage least squares pole-zero, linear prediction and sparse linear prediction methods, experimental results show that the proposed speech analysis method has lower spectral distortion, higher reconstruction SNR and sparser residuals.