Title :
Algorithms for high order hidden Markov modelling
Author_Institution :
Stellenbosch Univ., South Africa
Abstract :
We detail an algorithm that transforms any higher order hidden Markov model (HMM) to an equivalent first order HMM. This makes it possible to process higher order HMMs with standard techniques applicable to first order models. Based on this equivalence, a fast incremental algorithm is developed for training higher order HMMs from lower order approximations, thereby avoiding the training of redundant parameters. This makes training of high order HMMs practical for many applications
Keywords :
hidden Markov models; speech recognition; applications; fast incremental algorithm; first order HMM; high order hidden Markov modelling; lower order approximations; redundant parameters; speech recognition; training; Character recognition; Classification algorithms; Computational complexity; Computational efficiency; Databases; Hidden Markov models; History; Sparse matrices; Speech processing; Viterbi algorithm;
Conference_Titel :
Communications and Signal Processing, 1997. COMSIG '97., Proceedings of the 1997 South African Symposium on
Conference_Location :
Grahamstown
Print_ISBN :
0-7803-4173-2
DOI :
10.1109/COMSIG.1997.629990
