Speech analysis and segmentation by parametric filtering

A new set of digital signal processing techniques for detecting changes in a speech signal is considered. The overall approach is called parametric filtering, and it yields several promising new diagnostics for speech analysis and segmentation including in particular, the demodulated lag-one autocorrelation /spl gamma//sub /spl theta//(/spl eta/), the time-correlation analysis plot, and the /spl gamma//sub /spl theta//(/spl eta/)-based distortion measures. Initial experiments described in this paper establish the potential significance of the parametric filtering method and these new diagnostics for speech analysis and segmentation.