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- W1563317239 abstract "Modern speaker recognition applications require high accuracy at low complexity and easy calculation. In this paper, we propose a new method of text independent speaker recognition based on the use of the mean of the Mel frequency cepstral coefficients (MFCC) as a speaker model. These MFCC are extracted from the speaker phonemes in the pre-segmented speech sentences. A multi-layer neural network trained with the back propagation algorithm is proposed to classify these discriminative models. A study is carried out in order to view these models efficiency. Several experiments are made and show that the proposed method gives a high speaker recognition rate. Furthermore, throw these experiments; a technique is proposed to improve this recognition rate by an appropriate phonemes database selection." @default.
- W1563317239 created "2016-06-24" @default.
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- W1563317239 date "2004-06-10" @default.
- W1563317239 modified "2023-09-26" @default.
- W1563317239 title "Text independent speaker recognition using the mel frequency cepstral coefficients and a neural network classifier" @default.
- W1563317239 cites W2041823554 @default.
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- W1563317239 doi "https://doi.org/10.1109/isccsp.2004.1296479" @default.
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