Nonstationary latent Dirichlet allocation for speech recognition

Chuang Hua Chueh*, Jen-Tzung Chien

*此作品的通信作者

    研究成果: Conference article同行評審

    10 引文 斯高帕斯(Scopus)

    摘要

    Latent Dirichlet allocation (LDA) has been successful for document modeling. LDA extracts the latent topics across documents. Words in a document are generated by the same topic distribution. However, in real-world documents, the usage of words in different paragraphs is varied and accompanied with different writing styles. This study extends the LDA and copes with the variations of topic information within a document. We build the nonstationary LDA (NLDA) by incorporating a Markov chain which is used to detect the stylistic segments in a document. Each segment corresponds to a particular style in composition of a document. This NLDA can exploit the topic information between documents as well as the word variations within a document. We accordingly establish a Viterbi-based variational Bayesian procedure. A language model adaptation scheme using NLDA is developed for speech recognition. Experimental results show improvement of NLDA over LDA in terms of perplexity and word error rate.

    原文English
    頁(從 - 到)372-375
    頁數4
    期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
    出版狀態Published - 26 十一月 2009
    事件10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
    持續時間: 6 九月 200910 九月 2009

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