Continuous adaptive algorithms for pole-zero modeling of time-varying speech signals

Abstract

The main objective of this dissertation is to develop a pole-zero modeling algorithm which can accurately represent the spectral envelopes of speech signals. So far, most of the block processed pole-zero modeling algorithms yield a suboptimal estimation when input excitation is not known. And they cannot track the instantaneous variations of the signal parameters due to the nature of the algorithms which are developed for a block data processing. To alleviate these problems, we suggest several recursive pole-zero modeling algorithms, which can estimate the precise input excitation sequences and track the time-varying parameters of speech signals only from the observed time sequences. First, we propose a modified transversal Kalman filter (MTKF) algorithm which simultaneously estimates the unknown input excitation and the unknown parameters of a time-varying autoregressive moving average (ARMA) model based on the nonstationary transversal Kalman filter algorithm. The time variation of parameters is modeled as a random noise process whose statistics is assumed to be smoothly changing. And the second-order statistical information of the time variation is extracted from the Prediction errors. Accordingly, the input pitch pulses which induce on abrupt change in the prediction error are discriminated against the time variation of parameters. Therefore, the proposed algorithm can exactly detect pitch pulse instants and track time-varying parameters undisturbed by pitch pulses. In addition, we suggest a simplified version of the algorithm that can drastically reduce the computational complexity and has comparable performance to the original algorithm in the analysis of natural speech. Second, utilizing the well-known prewindowed least squares lattice (LSL) algorithm which is stable and has fast convergence speed, we propose a dual recursive least squares lattice(DRLSL) algorithm. In this algorithm, the autoregressive (AR) estimator and the moving average (MA) estimator...