Nonlinear prediction of speech

Abstract

Measurements were made of the correlation dimension of normally spoken speech from a single speaker, and the results reveal that most of the points in the state space of the signal lie very close to a manifold of a dimensionality of less than three. This result indicates that one should be able to construct a nonlinear predictor for speech that significantly outperforms linear predictors. To validate this conclusion, a nonparametric predictor was constructed which was able to produce a prediction gain approximately 3 dB better than an equivalent linear predictor. Similar improvements in signal-to-noise ratio were also observed when the nonlinear predictor was added to a simple speech coder.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>