Identification of Soundbite and Its Speaker Name Using Transcripts of Broadcast News Speech
Abstract
This article presents a pipeline framework for identifying soundbite and its speaker name from Mandarin broadcast news transcripts. Both of the two modules, soundbite segment detection and soundbite speaker name recognition, are based on a supervised classification approach using multiple linguistic features. We systematically evaluated performance for each module as well as the entire system, and investigated the effect of using speech recognition (ASR) output and automatic sentence segmentation. We found that both of the two components impact the pipeline system, with more degradation in the entire system performance due to automatic speaker name recognition errors than soundbite segment detection. In addition, our experimental results show that using ASR output degrades the system performance significantly, and that using automatic sentence segmentation greatly impacts soundbite detection, but has much less effect on speaker name recognition.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2010
- Source ID
- 10.1145/1731035.1731037
Entities
People
- Feifan Liu
- Yang Liu
Organizations
- Defense Advanced Research Projects Agency
- University of Texas at Dallas