Recurrence quantification analysis of speech

Abstract

A time-series analysis technique known as recurrence quantification analysis (RQA) holds promise as a new method for investigating the dynamical properties of the acoustic speech signal. RQA was created to deal with complex time series generated by high-dimensional, nonlinear dynamical systems. While RQA has most frequently been used for physiological studies and investigations into nonlinear dynamics, it has not been widely applied to the acoustic analysis of speech. Recurrence quantification analysis is free of many key assumptions involved in Fourier-type analyses such as data set size, stationarity, and linearity, which are routinely violated in the speech stream. This study offers some preliminary findings on the dynamic nature of speech and characteristics of speaker variability obtained by using recurrence quantification analysis. Future directions for the use of RQA in speech perception and production studies, including shadowing and imitation studies, are discussed.