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Title: A Study of Speaker Identification Using Irregular Decomposition Technique and Artificial Neural Network
Authors: 吳建達;林秉賦
Contributors: 車輛科技研究所
Keywords: Speaker identification;General regressive neural network;Wavelet packet transform
Date: 2007-11
Issue Date: 2014-04-29T07:32:38Z
Publisher: 中國機械工程學會; 中原大學
Abstract: A study of speaker identification for vehicle security systems based on the energy of speaker utterances is proposed in this paper. The proposed system consisted of a combination of signal pre-process, feature extraction using wavelet packet transform (WPT) and speaker identification using artificial neural network. In signal pre-process, the amplitude of utterances, for a same sentence, were normalized for preventing an error estimation caused by speakers' changed volume. In the feature extraction, three conventional methods were taking experiments and as comparisons with the irregular decomposition method in the proposed system. In order to verify the effect of the proposed system for identification, a general regressive neural network (GRNN) was used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system and were compared with the discrete wavelet transform (DWT), conventional WPT and WPT in Mel scale.
Relation: 2007 CSME CONF 中國機械工程學會第二十四屆全國學術研討會
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