Sciweavers

Share
IEICET
2008

Language Modeling Using PLSA-Based Topic HMM

11 years 5 months ago
Language Modeling Using PLSA-Based Topic HMM
In this paper, we propose a PLSA-based language model for sports live speech. This model is implemented in unigram rescaling technique that combines a topic model and an n-gram. In conventional method, unigram rescaling is performed with a topic distribution estimated from a history of recognized transcription. This method can improve the performance; however it cannot express topic transition. Incorporating concept of topic transition, it is expected to improve the recognition performance. Thus the proposed method employs a "Topic HMM" instead of a history to estimate the topic distribution. The Topic HMM is a Discrete Ergodic HMM that expresses typical topic distributions and topic transition probabilities. Word accuracy results indicate an improvement over tri-gram and PLSA-based conventional method using a recognized history.
Atsushi Sako, Tetsuya Takiguchi, Yasuo Ariki
Added 11 Dec 2010
Updated 11 Dec 2010
Type Journal
Year 2008
Where IEICET
Authors Atsushi Sako, Tetsuya Takiguchi, Yasuo Ariki
Comments (0)
books