SVM Classification Using Sequences of Phonemes and Syllables
SVM Classification Using Sequences of Phonemes and Syllables
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使用音素和音节序列的 SVM 分类
DOI：
10.1007/3-540-45681-3_31
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
S. Eickeler
中科院分区：
文献类型：
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作者：
G. Paass;Edda Leopold;M. Larson;J. Kindermann;S. Eickeler
在本文中，我们使用支持向量机对口头和书面文档进行分类。我们表明，通过利用子词单元串来提高书面材料的分类精度，对于小的主题类别具有显著的收益。使用子词单位对大类别的口语文献的分类仅略差于书面材料，小主题类别的降幅更大。最后，无损失地训练支持向量机关于从书面材料生成的音节并使用它们来对音频文档进行分类是可能的。我们的结果证实了支持向量机在稳健音频文档分类方面的强大前景，并表明支持向量机可以在一定程度上补偿语音识别错误，从而允许在系统中引入相当程度的主题独立性。
In this paper we use SVMs to classify spoken and written documents. We show that classification accuracy for written material is improved by the utilization of strings of sub-word units with dramatic gains for small topic categories. The classification of spoken documents for large categories using sub-word units is only slightly worse than for written material, with a larger drop for small topicc ategories. Finally it is possible, without loss, to train SVMs on syllables generated from written material and use them to classify audio documents. Our results confirm the strong promise that SVMs hold for robust audio document classification, and suggest that SVMs can compensate for speech recognition error to an extent that allows a significant degree of topic independence to be introduced into the system.