Novel weighting scheme for unsupervised language model adaptation using latent dirichlet allocation
نویسندگان
چکیده
A new approach for computing weights of topic models in language model (LM) adaptation is introduced. We formed topic clusters by a hard-clustering method assigning one topic to one document based on the maximum number of words chosen from a topic for that document in Latent Dirichlet Allocation (LDA) analysis. The new weighting idea is that the unigram count of the topic generated by hard-clustering is used to compute the mixture weights instead of using an LDA latent topic word count used in the literature. Our approach shows significant perplexity and word error rate (WER) reduction against the existing approach.
منابع مشابه
Unsupervised Language Model Adaptation Incorporating Named Entity Information
Language model (LM) adaptation is important for both speech and language processing. It is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsupervised LM adaptation, this paper investigates how effectively using named entity (NE) information, instead of considering all the words, helps LM adaptation. We ...
متن کاملUnsupervised language model adaptation using latent semantic marginals
We integrated the Latent Dirichlet Allocation (LDA) approach, a latent semantic analysis model, into unsupervised language model adaptation framework. We adapted a background language model by minimizing the Kullback-Leibler divergence between the adapted model and the background model subject to a constraint that the marginalized unigram probability distribution of the adapted model is equal t...
متن کاملRapid Unsupervised Topic Adaptation – a Latent Semantic Approach
In open-domain language exploitation applications, a wide variety of topics with swift topic shifts has to be captured. Consequently, it is crucial to rapidly adapt all language components of a spoken language system. This thesis addresses unsupervised topic adaptation in both monolingual and crosslingual settings. For automatic speech recognition we rapidly adapt a language model on a source l...
متن کاملLanguage model adaptation using latent dirichlet allocation and an efficient topic inference algorithm
We present an effort to perform topic mixture-based language model adaptation using latent Dirichlet allocation (LDA). We use probabilistic latent semantic analysis (PLSA) to automatically cluster a heterogeneous training corpus, and train an LDAmodel using the resultant topicdocument assignments. Using this LDA model, we then construct topic-specific corpora at the utterance level for interpol...
متن کاملDynamic Language Model Adaptation usin
We propose an unsupervised dynamic language model (LM) adaptation framework using long-distance latent topic mixtures. The framework employs the Latent Dirichlet Allocation model (LDA) which models the latent topics of a document collection in an unsupervised and Bayesian fashion. In the LDA model, each word is modeled as a mixture of latent topics. Varying topics within a context can be modele...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010