The use of time-domain selection for improved linear prediction

We show by theoretical argument and by experiment with both synthetic and real data that selection of an undriven segment of voiced speech for analysis by linear predictive coding (LPC) gives more accurate estimates of the poles of the vocal-tract model. In the case of voiced nasal phonemes, this technique provides a simple algorithm for separately determining the poles and the zeros in the model and illustrates the desirability of identifying the portions of the speech wave during which there is a significant driving input. A key problem which remains is the development of a practical algorithm for selecting such segments for analysis.