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Báo cáo hóa học: " A Statistical Approach to Automatic Speech Summarization Chiori Hori"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Statistical Approach to Automatic Speech Summarization Chiori Hori | EURASIP Journal on Applied Signal Processing 2003 2 128-139 2003 Hindawi Publishing Corporation A Statistical Approach to Automatic Speech Summarization Chiori Hori Department of Computer Science Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8552 Japan Email chiori@furui.cs.titech.ac.jp Sadaoki Furui Department of Computer Science Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8552 Japan Email furui@furui.cs.titech.ac.jp Rob Malkin Interactive Systems Labs Carnegie Mellon University Pittsburgh PA 15213 USA Email malkin@cs.cmu.edu Hua Yu Interactive Systems Labs Carnegie Mellon University Pittsburgh PA 15213 USA Email hua@cs.cmu.edu Alex Waibel Interactive Systems Labs Carnegie Mellon University Pittsburgh PA 15213 USA Email ahw@cs.cmu.edu Received 20 March 2002 and in revised form 11 November 2002 This paper proposes a statistical approach to automatic speech summarization. In our method a set of words maximizing a summarization score indicating the appropriateness of summarization is extracted from automatically transcribed speech and then concatenated to create a summary. The extraction process is performed using a dynamic programming DP technique based on a target compression ratio. In this paper we demonstrate how an English news broadcast transcribed by a speech recognizer is automatically summarized. We adapted our method which was originally proposed for Japanese to English by modifying the model for estimating word concatenation probabilities based on a dependency structure in the original speech given by a stochastic dependency context free grammar SDCFG . We also propose a method of summarizing multiple utterances using a two-level DP technique. The automatically summarized sentences are evaluated by summarization accuracy based on a comparison with a manual summary of speech that has been correctly transcribed by human subjects. Our experimental results indicate that the method we propose can effectively extract .