6533b862fe1ef96bd12c604c

RESEARCH PRODUCT

Learning the structure of HMM's through grammatical inference techniques

H. RulotEnrique VidalB. MasFrancisco Casacuberta

subject

Training setbusiness.industryComputer scienceEstimation theorySpeech recognitionMarkov processComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Pattern recognitionGrammar inductionsymbols.namesakeRule-based machine translationsymbolsArtificial intelligencePruning (decision trees)businessBaum–Welch algorithmHidden Markov modelError detection and correction

description

A technique is described in which all the components of a hidden Markov model are learnt from training speech data. The structure or topology of the model (i.e. the number of states and the actual transitions) is obtained by means of an error-correcting grammatical inference algorithm (ECGI). This structure is then reduced by using an appropriate state pruning criterion. The statistical parameters that are associated with the obtained topology are estimated from the same training data by means of the standard Baum-Welch algorithm. Experimental results showing the applicability of this technique to speech recognition are presented. >

yearjournalcountryeditionlanguage
2002-12-04International Conference on Acoustics, Speech, and Signal Processing
https://doi.org/10.1109/icassp.1990.115882