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中文摘要：
支持向量机的核函数因参数寻优问题,产生了额外计算量,从而降低了在语音识别应用系统中的实时性。鉴于以上弊端,在语音识别系统中,运用了一种基于切比雪夫多项式的核函数。该函数在训练过程中能够获得更少的支持向量个数,同时该函数结合了高斯核函数的优良性能,对广义的切比雪夫核函数进行了适当的改进得到修正切比雪夫核函数。实验运用了两个不同的语音数据库分别进行了对比实验,取得了较为理想的效果,提高了支持向量机的泛化能力及语音识别系统的鲁棒性。
英文摘要：
Owing to the parameter optimization, the kernel function of support vector machine will make additional computation, which reduces the real time of speech recognition system. In consideration of the above problem, a set of kernel functions for support vector machine is applied based on Chebyshev polynomials in the speech recognition system. The test results show that the generalized Chebyshev kernel approaches to the minimum support vector number for classification in general. Combining with the excellent performance of Gaussian kernel, generalized Chebyshev kernels are properly improved to obtain modified Chebyshev kernels. The experiments carry out comparative tests on two different speech databases and more satisfactory result is achieved. A generalization capability of SVM and robustness of speech recognition system is improved.
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