INTERSPEECH 2006 - ICSLP
Previous work has demonstrated the promise of frame-level quality measure methods to robust speaker recognition. This paper explores the issues involved in applying soft estimates to quality measures as weighting factors in score computation. A quality measure algorithm using Gaussian mixture density and Jensen divergence measure is presented for traditional GMM-UBM scoring mechanism. Derivation and validation of the quality measurement are reported in this paper. We investigate the usefulness of different feature processing, different GMM-based quality models and incorporation of divergence measure for quality estimation. Comparison experiments performed on the NIST1999 SRE corpus show the effectiveness of the proposed method.
Bibliographic reference. Zheng, Rong / Zhang, Shuwu / Xu, Bo (2006): "A quality measure method using Gaussian mixture models and divergence measure for speaker identification", In INTERSPEECH-2006, paper 1328-Wed3CaP.5.