A generalization of discrete hidden Markov model and of Viterbi algorithm

Author

Tao, Chongguang

Author_Institution

Dept. of Comput. Sci., State Univ. of New York, Buffalo, NY, USA

fYear

1992

fDate

30 Aug-3 Sep 1992

Firstpage

120

Lastpage

123

Abstract

The concepts of composite and basic symbols and composite and basic states are introduced and a generalized hidden Markov model is defined to allow variable length and depth of dependency. A recursive function is defined to compute the probability distribution of the transitions from basic or composite states to composite states. The Viterbi algorithm is generalized to compute the optimal state sequence given an observation sequence of length T with time cost of O (T×(max.(N, N_c))²), where N and N_c are the numbers of basic states and composite states respectively

Keywords

Markov processes; computational complexity; Viterbi algorithm; basic states; basic symbols; composite states; composite symbols; dependency; discrete hidden Markov model; generalization; recursive function; Computer science; Cost function; Distributed computing; Hidden Markov models; Probability distribution; Signal processing; Signal processing algorithms; Speech processing; Text recognition; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on

Conference_Location

The Hague

Print_ISBN

0-8186-2915-0

Type

conf

DOI

10.1109/ICPR.1992.201735

Filename

201735