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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18367

Title: Speaker Identification Based on the Frame Linear Predictive Coding Spectrum Technique
Authors: Wu, Jian-Da;Lin, Bing-Fu
Contributors: 車輛科技研究所
Keywords: Speaker identification;Linear predictive coding;Gaussian mixture model;General regression neural network
Date: 2009-05
Issue Date: 2014-04-29T07:28:17Z
Publisher: Elsevier Ltd
Abstract: In this paper, a frame linear predictive coding spectrum (FLPCS) technique for speaker identification is presented. Traditionally, linear predictive coding (LPC) was applied in many speech recognition applications, nevertheless, the modification of LPC termed FLPCS is proposed in this study for speaker identification. The analysis procedure consists of feature extraction and voice classification. In the stage of feature extraction, the representative characteristics were extracted using the FLPCS technique. Through the approach, the size of the feature vector of a speaker can be reduced within an acceptable recognition rate. In the stage of classification, general regression neural network (GRNN) and Gaussian mixture model (GMM) were applied because of their rapid response and simplicity in implementation. In the experimental investigation, performances of different order FLPCS coefficients which were induced from the LPC spectrum were compared with one another. Further, the capability analysis on GRNN and GMM was also described. The experimental results showed GMM can achieve a better recognition rate with feature extraction using the FLPCS method. It is also suggested the GMM can complete training and identification in a very short time.
Relation: Expert Systems with Applications, 36(4): 8056-8063
Appears in Collections:[車輛科技研究所] 期刊論文

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