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
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Automated generation of news content hierarchy by integrating audio, video, and text information
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
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
MM '09 Proceedings of the 17th ACM international conference on Multimedia
IEEE Transactions on Multimedia
Identification of Soundbite and Its Speaker Name Using Transcripts of Broadcast News Speech
ACM Transactions on Asian Language Information Processing (TALIP)
Extracting phrase patterns with minimum redundancy for unsupervised speaker role classification
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatic role recognition based on conversational and prosodic behaviour
Proceedings of the international conference on Multimedia
Speaker role recognition to help spontaneous conversational speech detection
Proceedings of the 2010 international workshop on Searching spontaneous conversational speech
Detecting forum authority claims in online discussions
LSM '11 Proceedings of the Workshop on Languages in Social Media
Detecting individual role using features extracted from speaker diarization results
Multimedia Tools and Applications
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Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.