所采用的基线系统为文本无关的说话人辨认系统。
The baseline system we used is text-independent speaker identification system.
实验结果表明：利用多特征组合多分类器的方法可以提高“文本无关”说话人辨认系统的识别率和可靠性。
The experimental results have shown that Combining Multiple Classifiers with different features can result in satisfactory and significant improvement in recognition performance.
说话人辨认系统的目的是提取、特征化和辨认表征说话人身份的语音信号信息，因此它在身份验证领域具有广阔的应用前景。
The goal of speaker identification systems is to extract, characterize and identify the information in the speech signal conveying speaker identity.
在说话人辨认实验中，较之传统的GMM方法，基于EGMM的系统的正识率提高了近3%，并且模型具有更小的平均尺寸。
Compared with traditional GMM, the correct recognition rate of si system based on EGMM increases by approximate 3%. Furthermore, the GMMS in new system have smaller average size.
在说话人辨认实验中，较之传统的GMM方法，基于EGMM的系统的正识率提高了近3%，并且模型具有更小的平均尺寸。
Compared with traditional GMM, the correct recognition rate of si system based on EGMM increases by approximate 3%. Furthermore, the GMMS in new system have smaller average size.
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