Voiced-unvoiced-silence detection using the Itakura LPC distance measure

Abstract

One of the most difficult problems in speech analysis is reliable discrimination among silence, unvoiced speech, and voiced speech which has been transmitted over a telephone line. Although several methods have been proposed for making this 3-level decision, these schemes have met with only modest success. In this paper a novel approach to the voiced-unvoiced-silence detection problem is proposed in which a spectral characterization of each of the 3 classes of signal is obtained during a training session, and an LPC distance metric and an energy distance are nonlinearly combined to make the final discrimination. This algorithm has been tested over conventional switched telephone lines, across a variety of speakers, and has been found to have an error rate of about 5%, with the majority of the errors (about 2/3) occurring at the boundaries between signal classes. The algorithm is currently being used in a speaker independent word recognition system.