頁籤選單縮合
題 名 | A Novel Characterization of the Alternative Hypothesis Using Kernel Discriminant Analysis for LLR-Based Speaker Verification |
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作 者 | Chao, Yi-hsiang; Wang, Hsin-min; Chang, Ruei-chuan; | 書刊名 | International Journal of Computational Linguistics & Chinese Language Processing |
卷 期 | 12:3 2007.09[民96.09] |
頁 次 | 頁255-272 |
分類號 | 312.85 |
關鍵詞 | Kernel fisher discriminant; Log-likelihood ratio; Speaker verification; Support vector machine; |
語 文 | 英文(English) |
英文摘要 | In a long-likelihood ratio (LLR)-based speaker verification system, the alternative hypothesis is usually difficult to characterize a priori, since the model should cover the space of all possible impostors. In this paper, we propose a new LLR measure in an attempt to characterize the alternative hypothesis in a more effective and robust way than conventional methods. This LLR measure can be further formulated as a non-linear discriminant classifier and solved by kernel-based techniques, such as the Kernel Fisher Discriminant (KFD) and Support Vector Machine (SVM). The results of experiments on two speaker verification tasks show that the proposed methods outperform classical LLR-based approaches. |
本系統中英文摘要資訊取自各篇刊載內容。