Joint optimization of model and excitation in parametric speech coders

This paper presents a new Analysis-by-Synthesis (AbS) technique for joint optimization of the excitation and model parameters based on minimizing the closed loop synthesis error instead of the linear prediction error. By minimizing the synthesis error, the analysis and synthesis stages become more compatible. Using a gradient search in the root domain, model parameters for a given excitation are optimized to minimize the error between the original and the synthesized speech. Since the optimization starts from the LPC solution, the synthesis error is guaranteed to be lower than that obtained using the LPC coefficients. For multipulse LPC, there is a 0.5–1 dB improvement in the segmental SNR for male and female speakers over 4 to 6 second long sentences. Listening tests and objective MOS scores confirm the improved speech quality. By adding an extra optimization step, the technique can be incorporated into the LPC, multi-pulse LPC and CELP-type speech coders.