Latent dirichlet language model for speech recognition

Jen-Tzung Chien*, Chuang Hua Chueh

*此作品的通信作者

研究成果: Conference contribution同行評審

23 引文 斯高帕斯(Scopus)

摘要

Latent Dirichlet allocation (LDA) has been successfully for document modeling and classification. LDA the document probability based on bag-of-words without considering the sequence of words. This discovers the topic structure at document level, is different from the concern of word prediction in recognition. In this paper, we present a new latent language model (LDLM) for modeling of word . A new Bayesian framework is introduced by the Dirichlet priors to characterize the uncertainty latent topics of n-gram events. The robust topic-based model is established accordingly. In the , we implement LDLM for continuous speech and obtain better performance than probabilistic semantic analysis (PLSA) based language method.

原文English
主出版物標題2008 IEEE Workshop on Spoken Language Technology, SLT 2008 - Proceedings
頁面201-204
頁數4
DOIs
出版狀態Published - 1 十二月 2008
事件2008 IEEE Workshop on Spoken Language Technology, SLT 2008 - Goa, India
持續時間: 15 十二月 200819 十二月 2008

出版系列

名字2008 IEEE Workshop on Spoken Language Technology, SLT 2008 - Proceedings

Conference

Conference2008 IEEE Workshop on Spoken Language Technology, SLT 2008
國家/地區India
城市Goa
期間15/12/0819/12/08

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