Decomposition of speech signals into deterministic and stochastic components

This paper presents a new method for decomposition of the speech signal into a deterministic and a stochastic component. The method is based on iterative signal reconstruction. The method involves: (1) separation of speech into an approximate excitation and filter components using linear predictive (LP) analysis; (2) identification of frequency regions of noise and deterministic components of excitation using cepstrum; (3) reconstruction of the two excitation components of the residual using an iterative algorithm; (4) and finally, the deterministic and stochastic components of the excitation are then obtained by combining the reconstructed frames of data using an overlap-add procedure. The deterministic and stochastic components are then passed through the time varying all-pole filter to obtain the components of the speech signal. The algorithm is able to decompose varying mixtures of stochastic and deterministic signals, like the noise bursts produced at the glottal closure and the deterministic glottal pulses. This new algorithm is a powerful tool for analysis of relevant features of the source component of speech signals.