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LSF-VQ Encoding Spedml Dim - PNN Algori" The major contributor to the overall bit rate in low rate speech coders is the encoding of the short-term spectral parameters. Current schemes typically use 32-40 bits per coding frame to represent this information using scalar quantization techniques. Several researchers have recently begun investigating the use of vector quantization (VQ) for these parameters, and found that direct VQ requires a prohibitively large vector codebook. We report here on some experiments undertaken on a large number of speech frames involving the use of vector quantization for the line spectral frequencies. Specifically, we set out to determine the influence of the clustering algorithm used in the training process and the number of training vectors required to give good generalization and hence a robust codebook.

A Comparisoat of Two Quanttization Techniques for Speech Spectral Parameters