| 摘要 |
PROBLEM TO BE SOLVED: To rescore a hypothesis of an ASR system by holding a training data amount of an acoustic model low and efficiently reflecting a wide speech context. SOLUTION: A method for preparing the wide context acoustic model includes a step for training an HMM having three states, a step for training a Bayesian network corresponding to a first state and a third state, and a step for combining HMM and a Bayesian network. The Bayesian netwok has a topology including first nodes q1 and q3 corresponding to the first and third states, second nodes C<SB>L</SB>and C<SB>R</SB>of unknown variables expressing a phoneme just before a first phoneme and a phoneme just after a final phoneme, and third nodes X<SB>1</SB>and X<SB>3</SB>of an observation space of the first and third states. COPYRIGHT: (C)2007,JPO&INPIT
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