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A Robust Text Dependent Speaker Verification System
在本論文中,我採用一些吵雜環境和安靜環境下和不同通道下的語料來做比對，在系統中，我加了音量的正規化，和倒頻譜正規化(包括Cepstral Mean Subtraction)並比較不同主機不同麥克風對本系統的影響。
By the prosperity of computer industry,people have higher requests for the security environment. Thus, the need for the speaker verification with the high distinguishing rate and low cost is indispensable. In general, in the quiets laboratory, it makes no difference that the speaker verification rate can be both reached the high distinguishing rate. However, the distinguishing rate in the different channel can be carried a lot. Therefore, to improve the distinguishing rate in the different channel is the major issue in this thesis.
In the thesis, a volume normalization and cepstral normalization is added to increase the speaker verification rate. We have test many voice data in quiet environment and also in noisy environment. We also test the speech in different channel. Simulation results show that using the cepstral normalization, can reduce the channel effect and increase the speaker verification rate. Using the volume normalization can also improve the speaker verification rate in quite environment.
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