Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Rules Behind Roles: Identifying Speaker Role in Radio Broadcasts
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Improving name tagging by reference resolution and relation detection
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information
IEEE Transactions on Computers
Initial study on automatic identification of speaker role in broadcast news speech
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Look who is talking: soundbite speaker name recognition in broadcast news speech
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Technical improvements of the E-HMM based speaker diarization system for meeting records
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Multistage speaker diarization of broadcast news
IEEE Transactions on Audio, Speech, and Language Processing
An overview of automatic speaker diarization systems
IEEE Transactions on Audio, Speech, and Language Processing
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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.