Complete optimization of excitation and model parameters in parametric speech coders
Khosrow Lashkari · The Journal of the Acoustical Society of America · 2001
In a speech coding system, synthesis error (the difference between the original speech at the encoder input and reproduced speech at the decoder output) is a more relevant measure of signal distortion than linear prediction (LP) error. By minimizing the synthesis error instead of the linear prediction error, the analysis and synthesis stages become more compatible. In a previous paper (J. Acoust. Soc. Am. 109, 2491), an Analysis-by-Synthesis (AbS) technique for joint optimization of the excitation and filter parameters in parametric speech coders were presented. Using a gradient search in the root domain, synthesis error is minimized by reoptimizing the filter parameters for a given excitation. This paper provides a major improvement over the previous work, resulting in more than 3 dB of gain in the segmental signal-to-noise ratio. The improvement is due to a new algorithm for computing the gradient vector in the gradient search procedure. The paper describes the new algorithm and report on the improved results. By adding an extra minimization step, this technique can be incorporated into the existing LPC, multipulse LPC, and CELP-type speech coding standards.