The baseline system we used is text-independent speaker identification system.
所采用的基线系统为文本无关的说话人辨认系统。
In a speaker identification system, training speaker models is computationally expensive, especially when the dimension of feature vectors is large.
在说话人识别系统中，通常训练说话人模型需要很长的时间，特别当说话人特征的维数很高时。
The background noise leads a mismatch between the training environment and testing environment, and degrades the performance of speaker identification system.
背景噪声的存在，使得说话人识别系统的训练环境和测试环境发生失配，导致系统性能发生急剧下降。
To solve the effect of channel changes on the performance of speaker identification system, apply the method of maximum a posteriori to specific channel compensation.
为了解决通道变化对说话人识别系统性能的影响，将最大后验概率方法应用到具体的通道补偿中。
Characteristic modeling is an important link in technology of speaker identification, a nice modeling method impact on the performance of speaker identification system.
建立声学模型是说话人识别技术的重要环节，一种好的建模方法对说话人识别系统的识别率具有极其重大的影响。
The experiments results of the closed-set text-independent speaker identification system indicate that the proposed models and algorithms improve identification accuracy.
闭集文本自由说话人辨认试验证明了提出的模型及其算法的正确性。
Combining Multiple Classifiers can be viewed as a novel hybrid system to achieve high recognition accuracy for Text Independent Speaker Identification.
组合多分类器可以看作是一种用于获得较高识别效果的混合系统。
Combining Multiple Classifiers can be viewed as a novel hybrid system to achieve high recognition accuracy for Text Independent Speaker Identification.
组合多分类器可以看作是一种用于获得较高识别效果的混合系统。
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