De-noising with Novel DWT-PNNGMM for Speaker Recognition
Zhengquan Qiu, Junxun Yin · 2006
In this paper, two modifications for speaker recognition are presented. The goal of de-noising is to remove the noise and to remain as much as possible the important features. Recently, signal de-noising using non-linear processing, for example, wavelet transformation have become increasingly popular. First, for threshold in the wavelet domain, a semi-soft threshold function that showed the advantages over hard and soft threshold function with respect to variance and bias of the estimated value is used. Gaussian Mixture Models (GMMs) require at least several minutes of training speech, which is not comfortable for real-world applications. On the other hand, Artificial Neural Networks (ANNs) based classifiers, show better performance for telephone speech and need less training data than the GMMbased ones. Second, PNN (Probabilistic Neural Networks) and GMM are combined to improve the performance of the system. The experiment is showed that the proposed method has more advantage for speaker recognition in noise circumstance.