TIME-SERIES ANALYSIS OF JITTER

This article concerns the time series analysis of jitter. Jitter involves small fluctuations in glottal cycle lengths. Time series analysis is the statistical processing of data that are recorded over a period of time. Conventionally, the amount of jitter in an analysis interval is estimated by a measure of dispersion of the glottal cycle lengths. The problem is that measures of dispersion only describe the fluctuations in the cycle lengths unambiguously when these are statistically independent. This means that the fluctuations are white noise and that changing the order of the cycles does not change their statistical properties. But it can be shown experimentally that neighbouring cycle lengths are not statistically independent because they are correlated. We therefore studied jitter by means of time series analysis methods. These dispense with the assumption that glottal cycle lengths are statistically independent. They make it possible to distinguish between mean- and short-term perturbations and to remove correlations between neighbouring perturbations. We studied dispersion measures of raw and whitened jitter (i.e., jitter from which correlations had been removed). Jitter time series were obtained from vowels [a], [i], [u] sustained by male and female healthy and dysphonic speakers. Results showed that the inter-speaker differences were smaller for whitened jitter than for raw jitter. Inter-speaker variability was reduced because time series analysis separated random from non-random perturbations.