Simultaneous estimation of poles and zeros in speech analysis and ITIF-iterative inverse filtering algorithm

A new algorithm is proposed for the simultaneous estimation of poles and zeros in speech analysis. The algorithm is based on estimating the unknown system input and improvement of this estimate through the iterations. Thus, the algorithm does not require any type of preliminary deconvolution of the speech waveform, such as synchronization with pitch period or homomorphic deconvolution. Detailed analysis of a simulated system, as well as a preliminary analysis of initial nasal consonants /m/ and /n/, are presented. These analyses have shown that the ITIF algorithm gives a very accurate fit of the spectra of the systems analyzed. The iterative inverse filtering algorithm (ITIF) is a new technique for modeling linear systems having unknown input with a flat spectral envelope, such as pulse train or white noise input, by applying the pole-zero model. The ITIF algorithm in each iteration solves two linear parameter estimation problems: in the first one the unknown system input is estimated; in the second one the estimated input is used to determine the parameters of the pole-zero model. Experiments made so far have shown that the algorithm converges in a small number of iterations.