Compensating for Mismatch in High-Level Speaker Recognition

William M. Campbell
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引用次数: 13

Abstract

Speaker recognition using high-level features has been a successful area of exploration. Features obtained from many different levels-phones, words, prosodic events, etc.-are used to characterize the speaker. A good modeling technique for these features is the support vector machine (SVM). SVMs model the n-gram frequencies from speaker utterances in a high-dimensional SVM feature space and have shown excellent performance over a wide variety of high-level features. A complimentary method of recent exploration in SVM speaker recognition is the use of nuisance attributes projection (NAP). NAP removes directions from SVM feature space that are superfluous to the task of speaker recognition-channel information, session variability, etc. In this paper, we consider the application of NAP to high-level speaker recognition. We describe the difficulties in applying this method and propose solutions. We also conduct experiments showing that NAP can reduce variability in SVM feature space leading to improved performance
高阶说话人识别中的失配补偿
利用高层特征识别说话人已经是一个成功的探索领域。从许多不同层次获得的特征——电话、单词、韵律事件等——被用来描述说话人的特征。支持向量机(SVM)是这些特征的一个很好的建模技术。支持向量机在高维特征空间中对说话人话语的n-gram频率进行建模,并在各种高级特征上表现出优异的性能。在支持向量机的说话人识别中,最近探索的一种补充方法是使用干扰属性投影(NAP)。NAP从SVM特征空间中去除对说话人识别任务(信道信息、会话可变性等)来说是多余的方向。在本文中,我们考虑将NAP应用于高层次的说话人识别。我们描述了应用这种方法的困难,并提出了解决办法。我们还进行了实验,表明NAP可以减少SVM特征空间的可变性,从而提高性能
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