基于hmm的变分贝叶斯学习文本分割及其在视听索引中的应用

Takafumi Koshinaka, Akitoshi Okumura, Ryosuke Isotani
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引用次数: 0

摘要

近年来,大词汇量连续语音识别(LVCSR)的研究进展使得信息检索技术应用于结果文本成为可能。提出了一种新的无监督文本分割方法。假设文本流的生成模型是一个从左到右的隐马尔可夫模型(HMM),文本分割可以表述为利用文本流进行模型参数估计和模型选择。该公式是基于变分贝叶斯框架导出的,该框架有望很好地处理高度稀疏的数据,如文本。通过一系列实验证明了该方法的有效性,其中广播新闻节目被自动转录并分割成单独的新闻故事。©2007 Wiley期刊公司电子工程学报,2009,29 (1):1-11;在线发表于Wiley InterScience (www.interscience.wiley.com)。DOI 10.1002 / ecjb.20421
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HMM-based text segmentation using variational Bayes learning and its application to audio-visual indexing

Recent progress in large-vocabulary continuous speech recognition (LVCSR) has raised the possibility of applying information retrieval techniques to the resulting text. This paper presents a novel unsupervised text segmentation method. Assuming a generative model of a text stream as a left-to-right hidden Markov model (HMM), text segmentation can be formulated as model parameter estimation and model selection using the text stream. The formulation is derived based on the variational Bayes framework, which is expected to work well with highly sparse data such as text. The effectiveness of the proposed method is demonstrated through a series of experiments, where broadcast news programs are automatically transcribed and segmented into separate news stories. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(12): 1–11, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20421

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