A time-varying analysis method for rapid transitions in speech

A linear predictive coding (LPC) model based on time-dependent poles which has yielded promising results when applied to synthetic data is applied to real speech data. The data are processed pitch-synchronously using a simple procedure to identify regions of the data that best fit the model. The maximum-likelihood technique, which has been found to be robust in the presence of noise, is used to estimate the parameters. Resulting formant estimates for several diphthongs are presented. The algorithm tracks the formants well, both in stable regions and in regions of transition. This ability to track formant variation within analysis intervals is a definite advantage over traditional LPC. Results from speech data involving final stop consonants are presented. Rapid changes, particularly in the first and second formants, in the region immediately prior to the stop are detected. Such abrupt transitions are often not detected by traditional time-invariant methods