Speaker Diarization: Towards a More Robust and Portable System

In this paper, we describe a new method for speaker segmentation and clustering of an audio document. For the segmentation phase, we combine the generalized likelihood ratio (GLR) and the Bayesian information criterion (BIC) in a way that avoids most of the parameters tuning. For the clustering phase, we use an existing approach that utilizes the eigen vector space model (EVSM) with a bottom-up hierarchical grouping but we make some improvements by introducing prosodic information. Evaluation is done on the audio database of the ESTER evaluation campaign for the rich transcription of French Broadcast news. Results show that our method which operates without any a priori knowledge about speakers is suitable for speaker diarization as it outperforms the traditional ones with an overall diarization error rate (DER) of 16.72%.