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中文摘要：
利用纠错输出编码的矩阵编码构造出若干个无关的子支持向量机，用来改善分类模型的整体容错性能。使用了一对余、一对一、稠密型随机编码、稀疏型随机编码4种常用的纠错输出编码方法，用于训练集和测试集。实验结果显示，对于韩语非特定人小词汇量孤立词的语音识别，基于纠错输出编码的支持向量机比隐马尔科夫方法具有更高的识别率。其中，一对一编码是效果最好的。
英文摘要：
A method was proposed based on application of Error Correcting Output Codes Support Vector Machine （ECOC-SVM） in order to get better results of speech recognition. Some uncorrelated SVMs were constructed based on ECOC matrix codes to improve the integrated performance of fault tolerance of classification model. This paper gives four commonly used encod- ings of ECOC. One versus the rest, One versus one, Dense random and Sparse random. By comparing the results with these of speech recognition based on Hidden Markov Model, the experiments indicate that the ECOC method was more suitable for isolated words of a small vocabulary in Korean spoken by non-specific persons, among which the predicting accuracy of one-versus-one was the highest of all.
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