Wavelet feature domain adaptive noise reduction using learning algorithm for text-independent speaker recognition

In this paper, a type of thresholding method is developed for adaptive noise reduction. Here, we propose a new type thresholding method. Unlike the standard thresholding functions, the new thresholding functions are infinitely differentiable. Gradient-based adaptive learning algorithms are presented to seek the optimal solution for noise reduction. Furthermore, the learning algorithm can be used for any speaker data derived from discrete wavelet transform. It is demonstrated that 94% correct classification rates can be achieved by the use of the first 32 variation features in TALUNG database.