Improving Speaker Diarization

This paper describes the LIMSI speaker diarization system used in the RT-04F evaluation. The RT-04F system builds upon the LIMSI baseline data partitioner, which is used in the broadcast news transcription system. This partitioner provides a high cluster purity but has a tendency to split the data from a speaker into several clusters when there is a large quantity of data for the speaker. In the RT-03S evaluation the baseline partitioner had a 24.5% diarization error rate. Several improvements to the baseline diarization system have been made. A standard Bayesian information criterion (BIC) agglomerative clustering has been integrated replacing the iterative Gaussian mixture model (GMM) clustering; a local BIC criterion is used for comparing single Gaussians with full covariance matrices. A second clustering stage has been added, making use of a speaker identification method: maximum a posteriori adaptation of a reference GMM with 128 Gaussians. A final post-processing stage refines the segment boundaries using the output of the transcription system. Compared to the best configuration baseline system for this task, the improved system reduces the speaker error time by over 75% on the development data. On evaluation data, a 8.5% overall diarization error rate was obtained, a 60% reduction in error compared to the baseline.