Mixture excitations and finite-state CELP speech coders
Adil Benyassine, H. Abut · 1992
Code excited linear prediction (CELP) coding and its derivatives are currently the most frequently used techniques for speech compression at medium-to-low rate range. Until this study, the excitation vectors were always selected from a codebook generated by a Gaussian source or by the ensemble of residual signals collected from the used speech database. To the best of the author's knowledge, there is no study in which the excitation vectors were formed from a mixture of sources, a notion very successfully used by the speech recognition community within the hidden Markov model (HMM) framework. The authors proposed an improvement to the excitation model of CELP coders by embedding a labeled-state finite state vector quantization (FSVQ) and a mixture density approach in constructing 5-ms-long excitation vectors.>