Robust spectral parameter coding in speech processing
Nadim Batri · Library and Archives Canada (Government of Canada) · 1998
Linear predictive coding (LPC) is employed in many low bit rate speech coders. LPC models the short-term spectral information for a block of speech using an all-pole response. Line spectral frequencies (LSF) have been found to be an effective parametric representation for the all-pole response. Vector quantization (VQ) is often used to code the coefficients of the response. VQ performs poorly whenever it is coding coefficient vectors which are not well matched to the distribution of its codebooks. A shift in the distribution can be caused by filtering (microphones, filters in communication equipment, etc.), speaker or environmental variability (male, female, background noise, etc.). In this thesis, we explore a method for matching the distribution of the vectors representing the incoming speech signal to the distribution of the codebooks. A novel mapping model based on the transformation of codebooks using the mean and the standard deviation of the distributions is used to increase the robustness of vector quantization. The mapping model is optimized in two ways---choosing the most suitable spectral parameter representation and seeking the best way to obtain the form of the mapping model. The effectiveness and limitations of this method are investigated through simulation of a split vector quantizer (SVQ) of the LPC coefficients.