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中文摘要：
一个修改 Parzen 窗户方法，在低频率使分辨率高并且把光滑放在高频率，被建议获得统计模型。然后，当长句子被处理时，利用统计模型的一个性分类方法被建议，它有性分类的 98% 精确性。由男声音和女性表示的分离，与不同情感训练样品的讲话的平均数和标准差被用来创造相应情感模型。然后在测试样品和沥青的统计模型之间的 Bhattacharyya 距离，在沥青的 speech.The 正规化为情感识别被利用因为男声音和女声音也被考虑，以便说明他们直到一个一致空格。最后，讲话情感识别实验基于 K 最近的邻居显示出那， 81% 的正确的率被完成，在它仅仅是 73.85%if 的地方，传统的参数被利用。
英文摘要：
A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech. The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85% if the traditional parameters are utilized.
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