Speech analysis using the weighted recursive least squares algorithm with a variable forgetting factor

Abstract

A weighted recursive least-squares algorithm with a variable forgetting factor (WRLS-VFF) is introduced for speech signal analysis. The variable forgetting factor, which indicates the state change of the estimator, can be used to estimate the input excitation when the input is either white noise or periodic pulse trains. Two analysis techniques are examined: glottal closed-phase adaptive formant tracking and glottal closed-phase inverse filtering. The glottal closed-phase interval can be located approximately from the VFF estimation error. The data analyzed include synthesized speech segments and isolated words and sentences from real speech. Results show that the WRLS-VFF algorithm offers a more accurate estimation of formants and faster formant tracking than either linear predictive coding or several other adaptive algorithms. In addition, the WRLS-VFF technique is used to obtain, automatically, estimates of the glottal volume-velocity waveform by inverse filtering. >