摘要
When using thresholding method to discrete wavelet transform, a fixed threshold is not suitable if the intra-speaker and inter-speaker data variation is large. Here, we propose a new variation thresholding method. The method requires adaptive weighted to be automatically selected. Furthermore, the variation thresholding can be use to any speaker data derived from discrete wavelet transform. It is demonstrated that 93% correct classification rates can be achieved by the use of the first 32 variation features.
延伸閱讀
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