Abstract

In this paper a text independent speaker recognizer from controlled noisy speech signals has been investigated. A recorded data is used for 20 Kurdish speakers (10 males, and 10 females) . The feature used in this work is the MFCC, and k-NN is used as a classifier. The recognition performance from the noisy speech signals has been improved by a de-noising technique using wavelet transform. The result show that the de-noising technique could improve the performance of speaker recognizer by about 36%.

References

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