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Experimental condition

To test an Ergodic HMM as an SNG, experiments were performed. As training datum, we used the ATR Dialog Database. The example sentences are shown in Table  1 . The learning conditions of the experiments are shown in Table 2 .


表 1: Example of sentences
$\bullet$ hai moshimoshi
(Hello)
$\bullet$ e-tto sochira dai ichi kai no tsuuyaku deNwa kokusai kaigi no
jimu kyoku de sho u ka
(Well, is this the office of the first international conference on
interpreting telephony ?)
$\bullet$ hai sou desu ( Yes )
$\bullet$ e-tto chotto sono kaigi no koto dene
(Well about this conference ...)
$\bullet$ hai douzo (Yes...)
$\bullet$ e-tto ima temoto ni ano touroku youshi ga aru N desu keredomo
(I have a registration form, now.)


表 2: Learning conditions for Ergodic HMM
type of Hidden Markov model Mealy type
learning sentence number 8475
total word number 57354 words
number of states 2, 4, 8 and 16
vocabulary 6418
Initial state distribution equivalent value
Initial transition value random
Initial symbol value random
end of HMM learning under 1% likely hood

図 1: Results of an 8-state Ergodic HMM analysis
\epsfig{file=8st-L-model.ps,height=60mm}


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次へ: How to analyze the 上へ: Automatic Acquisition on Stochastic 戻る: Automatic Acquisition on Stochastic
Jin'ichi Murakami 平成13年1月19日