Some relations among stochastic finite state networks used inautomatic speech recognition
Casacuberta, F.
Dept. of Sistemas Inf. y Comput., Universidad Politecnica de Valencia;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 1990
Volume: 12,
Issue: 7
On page(s): 691-695
ISSN: 0162-8828
References Cited: 26
CODEN: ITPIDJ
INSPEC Accession Number: 3735410
Digital Object Identifier: 10.1109/34.56212
Current Version Published: 2002-08-06
Abstract
In the literature on automatic speech recognition, the popular
hidden Markov models (HMMs), left-to-right hidden Markov models
(LRHMMs), Markov source models (MSMs), and stochastic regular grammars
(SRGs) are often proposed as equivalent models. However, no formal
relations seem to have been established among these models to date. A
study of these relations within the framework of formal language theory
is presented. The main conclusion is that not all of these models are
equivalent, except certain types of hidden Markov models with
observation probability distribution in the transitions, and stochastic
regular grammar
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