Structured Support Vector Machines for Noise Robust Continuous Speech Recognition
Structured Support Vector Machines for Noise Robust Continuous Speech Recognition
复制标题
用于噪声鲁棒连续语音识别的结构化支持向量机
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
M. Gales
中科院分区：
文献类型：
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作者：
Shi;M. Gales
区别模型的使用是语音识别生成模型的一个有趣的替代方案。本文研究了这些模型中的一种形式，结构化支持向量机(SVMs)，用于抗噪语音识别。结构化支持向量机的一个重要方面是联合特征空间的形式。在这项工作中，使用了基于产生式模型的特征，这使得基于模型的补偿方案可以应用于产生健壮的关节特征。然而，这些功能需要指定将帧分割为单词或子词。在以前的工作中，这种分割是使用产生式模型获得的。在此，使用结构化支持向量机的参数对分割进行细化。描述了一种用于获得“最佳”分割的维特比利式方案，以及对训练算法的修改以允许它们被有效地使用。该方法的性能是在噪声污染的连续数字任务：Aurora 2上进行的评估。版权所有©2011 ISCA。
The use of discriminative models is an interesting alternative to generative models for speech recognition. This paper examines one form of these models, structured support vector machines (SVMs), for noise robust speech recognition. One important aspect of structured SVMs is the form of the joint feature space. In this work features based on generative models are used, which allows model-based compensation schemes to be applied to yield robust joint features. However, these features require the segmentation of frames into words, or subwords, to be specified. In previous work this segmentation was obtained using generative models. Here the segmentations are refined using the parameters of the structured SVM. A Viterbilike scheme for obtaining "optimal" segmentations, and modifications to the training algorithm to allow them to be efficiently used, are described. The performance of the approach is evaluated on a noise corrupted continuous digit task: AURORA 2. Copyright © 2011 ISCA.