Perceptual enhancement of CELP speech coders

The dominant technique in present day low bit rate speech coders is based on the use of voice production models in which vocal tract filters are excited by vectors chosen from fixed and adaptive codebooks. It has long been recognized that to improve the perceptual quality of such coders it is necessary to also allow for the psychoacoustic properties of the human ear. The weighting filter traditionally used for this purpose is sub-optimal as it doesn´t explicitly evaluate auditory characteristics. In this paper we replace the weighting filter with an auditory model which enables the search for the optimum stochastic code vector in the psychoacoustic domain. The algorithm, which has been termed PERCELP (for perceptually enhanced random codebook excited linear prediction), produces speech that is of considerably better quality than obtained with a weighting filter. The computational overhead is low enough to warrant the use of this approach in new speech coders