Study on Speaker Recognition Using Decision Tree And K-Nearest Neighbor
Date Issued
2014
Date
2014
Author(s)
Lee, Min-yeah
Abstract
In recent years, getting through highly developing of technology, 3C products like smart phones and tablets had been spread around our daily life. Whatsoever, the situation as described means it will not be secure to identify someone by using ones password. Meanwhile like that of developing technology, the biology identification technique had been also largely improved. Moreover, among all features of creatures, voice is regarded as one of the most important characteristics, which means we can adopt voiceprint as an efficient tool to recognize people.
Regarding to the algorithms, machine-learning has been mostly used by the studies in the aspect of categorization and identification. The K-Nearest Neighbor, KNN adopted in this study has advantages of the ability to well categorize and predict, thus, it is also common in studies of voice identification. Once we set parameters well, it can lead us to a good outcome by its ability of identification.
Though K-Nearest Neighbor and KNN are working quite well in the aspect of voice identification, however, if we distribute train data and test data beforehand, the effect of identification can be much improved. Consequently, decision tree was adopted in the study to categorize train data and test data in recorded sentence, and then combine the data and execute with KNN. The final outcome, as expected, has been improved.
Subjects
K最近鄰居法
決策樹
梅爾倒頻譜係數
語者辨識
Type
thesis
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