Adaptive analysis of speech based on a pole-zero representation

Two analysis methods are proposed to estimate the orders as well as the parameter values of an autoregressive moving-average (ARMA) process with a white Gaussian noise or a train of impulses as input. This process is adopted as a model for the process of speech production. The estimation is accomplished directly from the signal waveform using recursive formulas and is optimum in the sense that the variance of errors in the estimated waveform is minimized. This method is less vulnerable to fluctuations in the fundamental period of the source than the previously proposed methods based on the auto-correlation function. In order to trace the trajectories of the poles and zeros in the vocal tract transfer function, it is required to develop a method for adapting to the temporal variations of the acoustic characteristics of speech. A method is also proposed for adaptive control of the analysis interval based on the detection of convergence of the estimated values of the parameters. The validity of the proposed methods is tested by analysis of both synthetic and natural speech sounds.