Paper: Initial Study On Automatic Identification Of Speaker Role In Broadcast News Speech

ACL ID N06-2021
Title Initial Study On Automatic Identification Of Speaker Role In Broadcast News Speech
Venue Human Language Technologies
Session Short Paper
Year 2006
Authors
  • Yang Liu (University of Texas at Dallas, Richardson TX)

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 base- line of around 50%) using the human tran- scriptions 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.