Infinite structured support vector machines for speech recognition
Infinite structured support vector machines for speech recognition
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DOI：
10.1109/icassp.2014.6854215
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Jingzhou Yang;R. V. Dalen;Shi-Xiong Zhang;M. Gales
中科院分区：
文献类型：
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作者：
Jingzhou Yang;R. V. Dalen;Shi-Xiong Zhang;M. Gales
判别模型，如支持向量机（SVM），已成功地应用于语音识别和提高性能。一个贝叶斯非参数版本的SVM，无限的SVM，通过允许更灵活的决策边界的SVM改进。然而，像SVM一样，无限SVM分别对每个类进行建模，这限制了它们一次只能对一个词进行分类。SVM的一个推广是结构化SVM，其类可以是共享参数的单词序列。本文研究了贝叶斯非参数和结构化模型的结合。详细讨论了无限结构化支持向量机的一个具体实例，它将无限结构化支持向量机的优点应用到连续语音识别中。
Discriminative models, like support vector machines (SVMs), have been successfully applied to speech recognition and improved performance. A Bayesian non-parametric version of the SVM, the infinite SVM, improves on the SVM by allowing more flexible decision boundaries. However, like SVMs, infinite SVMs model each class separately, which restricts them to classifying one word at a time. A generalisation of the SVM is the structured SVM, whose classes can be sequences of words that share parameters. This paper studies a combination of Bayesian non-parametrics and structured models. One specific instance called infinite structured SVM is discussed in detail, which brings the advantages of the infinite SVM to continuous speech recognition.