A DNN-HMM approach to story segmentation

Jia Yu, Xiong Xiao, Lei Xie, Eng Siong Chng, Haizhou Li

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations

Abstract

Hidden Markov model (HMM) is one of the popular techniques for story segmentation, where hidden Markov states represent the topics, and the emission distributions of n-gram language model (LM) are dependent on the states. Given a text docu-ment, a Viterbi decoder finds the hidden story sequence, with a change of topic indicating a story boundary. In this paper, we propose a discriminative approach to story boundary detection. In the HMM framework, we use deep neural network (DNN) to estimate the posterior probability of topics given the bag-of-words in the local context. We call it the DNN-HMM approach. We consider the topic dependent LM as a generative modeling technique, and the DNN-HMM as the discriminative solution. Experiments on topic detection and tracking (TDT2) task show that DNN-HMM outperforms traditional n-gram LM approach significantly and achieves state-of-the-art performance.

Original languageEnglish
Pages (from-to)1527-1531
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume08-12-September-2016
DOIs
StatePublished - 2016
Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
Duration: 8 Sep 201616 Sep 2016

Keywords

  • Deep neural network
  • Hidden Markov model
  • Story segmentation

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