On pole-zero modeling of speech

Three different types of pole-zero modeling have been investigated. The main concept lying in these methods is to fit a high order pole predictor to the speech spectrum, and then to decompose the resulting predictor into a pole predictor and a zero predictor. To obtain the predictor parameters by Padé approximation, either Trench algorithm or Berlekamp-Massey algorithm can be used for decomposition. In addition to Padé approximation method, the generalized inverse method and the modified Durbin´s method are also considered for pole-zero modeling. Preliminary results show that the proposed methods yield better fitting of speech spectral envelope, especially of spectral nulls, than the all-pole model.