A covariance kernel for svm language recognition
A covariance kernel for svm language recognition
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DOI：
10.1109/icassp.2008.4518566
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发表时间：
2008-05
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影响因子：
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通讯作者：
W. Campbell
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文献类型：
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作者：
W. Campbell
语言识别的区别性训练已经成为提高系统性能的关键工具。此外，直接从移位增量倒谱特征进行识别已被证明是有效的。一个成功的例子是基于GMM均值超向量的支持向量机语言识别。GSV是通过通用背景模型(UBM)GMM的地图适配来创建的。这项工作通过将超矢量框架扩展到UBM的协方差来对这一思想提出了一种新的扩展。我们展示了一种新的包含这种协方差结构的支持向量机核。此外，我们还提出了一种将支持向量机模型参数推回到GMM模型的方法。这些GMM模型可以用作另一种评分形式。新方法在一项14种语言的任务上得到了演示，与以前的技术相比，性能有了显著的提高。
Discriminative training for language recognition has been a key tool for improving system performance. In addition, recognition directly from shifted-delta cepstral features has proven effective. A successful example of this paradigm is SVM-based discrimination of languages based on GMM mean supervectors (GSVs). GSVs are created through MAP adaptation of a universal background model (UBM) GMM. This work proposes a novel extension to this idea by extending the supervector framework to the covariances of the UBM. We demonstrate a new SVM kernel including this covariance structure. In addition, we propose a method for pushing SVM model parameters back to GMM models. These GMM models can be used as an alternate form of scoring. The new approach is demonstrated on a fourteen language task with substantial performance improvements over prior techniques.