A DNN-HMM Approach to Story Segmentation

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

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 document, 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.

DOI: 10.21437/Interspeech.2016-873

Cite as

Yu, J., Xiao, X., Xie, L., Chng, E.S., Li, H. (2016) A DNN-HMM Approach to Story Segmentation. Proc. Interspeech 2016, 1527-1531.

author={Jia Yu and Xiong Xiao and Lei Xie and Eng Siong Chng and Haizhou Li},
title={A DNN-HMM Approach to Story Segmentation},
booktitle={Interspeech 2016},