Tree coding of speech has been investigated by several workers. Virtually all of these investigations have involved incremental tree coding in that no matter how deep the tree is searched, only a single path map symbol is released at a time. As noted by Gray, even if a good long-term fit is found, the first step in the fit may be a poor one, thus yielding large sample distortions. Hence, it is important to stay on a path long enough to achieve the promised long-term distortion value. The relative frequency of path switching for the single symbol release rule is investigated for the(M,L)and truncated Viterbi tree search algorithms, various search depths, and different code generators. In addition, two multiple symbol release rules are investigated. One rule releases a fixed number of path symbols at a time, while the other rule releases a variable number of path symbols, the exact number depending on how many symbols are required for the average sample distortion to be less than or equal to theL-depth path average distortion. Speech sources are considered exclusively.

Optimal estimation algorithms for the identification of the predictor coefficients for both undistorted and distorted speech are developed by first specifying appropriate state space message and observation models. Those portions of the models and the algorithms which impede their utility are discussed. A technique for identifying the predictor coefficient vector single-stage state transition matrix is derived, and experimental results using speech data are presented.