標題: Using Kernel Discriminant Analysis to Improve the Characterization of the Alternative Hypothesis for Speaker Verification
作者: Chao, Yi-Hsiang
Tsai, Wei-Ho
Wang, Hsin-Min
Chang, Ruei-Chuan
資訊工程學系
Department of Computer Science
關鍵字: Kernel Fisher Discriminant (KFD);likelihood ratio;speaker verification;support vector machine (SVM)
公開日期: 1-Nov-2008
摘要: Speaker verification can be viewed as a task of modeling and testing two hypotheses: the null hypothesis and the alternative hypothesis. Since the alternative hypothesis involves unknown impostors, it is usually hard to characterize a priori. In this paper, we propose improving the characterization of the alternative hypothesis by designing two decision functions based, respectively, on a weighted arithmetic combination and a weighted geometric combination of discriminative information derived from a set of pretrained background models. The parameters associated with the combinations are then optimized using two kernel discriminant analysis techniques, namely, the kernel Fisher discriminant (KFD) and support vector machine (SVM). The proposed approaches have two advantages over existing methods. The first is that they embed a trainable mechanism in the decision functions. The second is that they convert variable-length utterances into fixed-dimension characteristic vectors, which are easily processed by kernel discriminant analysis. The results of speaker-verification experiments conducted on two speech corpora show that the proposed methods outperform conventional likelihood ratio-based approaches.
URI: http://dx.doi.org/10.1109/TASL.2008.2004297
http://hdl.handle.net/11536/8229
ISSN: 1558-7916
DOI: 10.1109/TASL.2008.2004297
期刊: IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Volume: 16
Issue: 8
起始頁: 1675
結束頁: 1684
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