Application of an LPC distance measure to the voiced-unvoiced-silence detection problem

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 three-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 three classes of signal is obtained during a training session, and an LPC distance measure 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 percent, with the majority of the errors (about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\frac{2}{3}</tex> ) occurring at the boundaries between signal classes. The algorithm is currently being used in a speaker-independent word recognition system.