Language Modeling Using PLSA-Based Topic HMM
نویسندگان
چکیده
منابع مشابه
Language modeling using PLSA-based topic HMM
In this paper, we propose a PLSA-based language model for sports-related live speech. This model is implemented using a unigram rescaling technique that combines a topic model and an n-gram. In the conventional method, unigram rescaling is performed with a topic distribution estimated from a recognized transcription history. This method can improve the performance, but it cannot express topic t...
متن کاملStyle & Topic Language Model Adaptation Using HMM-LDA
Adapting language models across styles and topics, such as for lecture transcription, involves combining generic style models with topic-specific content relevant to the target document. In this work, we investigate the use of the Hidden Markov Model with Latent Dirichlet Allocation (HMM-LDA) to obtain syntactic state and semantic topic assignments to word instances in the training corpus. From...
متن کاملStyle And Topic Language Model Adaptation Using HMM-LDA
Adapting language models across styles and topics, such as for lecture transcription, involves combining generic style models with topic-specific content relevant to the target document. In this work, we investigate the use of the Hidden Markov Model with Latent Dirichlet Allocation (HMM-LDA) to obtain syntactic state and semantic topic assignments to word instances in the training corpus. From...
متن کاملSelection-Based Language Model for Domain Adaptation using Topic Modeling
This paper introduces a selection-based LM using topic modeling for the purpose of domain adaptation which is often required in Statistical Machine Translation. The performance of this selection-based LM slightly outperforms the state-of-theart Moore-Lewis LM by 1.0% for EN-ES and 0.7% for ES-EN in terms of BLEU. The performance gain in terms of perplexity was 8% over the Moore-Lewis LM and 17%...
متن کاملPLSA-based topic detection in meetings for adaptation of lexicon and language model
A topic detection approach based on a probabilistic framework is proposed to realize topic adaptation of speech recognition systems for long speech archives such as meetings. Since topics in such speech are not clearly defined unlike news stories, we adopt a probabilistic representation of topics based on probabilistic latent semantic analysis (PLSA). A topical sub-space is constructed by PLSA,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2008
ISSN: 0916-8532,1745-1361
DOI: 10.1093/ietisy/e91-d.3.522