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- W2385623829 abstract "A new feature vectorMel Frequency Principal Coefficient (MFPC), used for speaker recognition is proposed. It is derived from Principal Component Analysis on the Mel Scale Spectrum Vector. The correlation information among different frequency channels, which is mainly caused by the vocal tract resonance, can be efficiently exploited by means of MFPC. This correlation information has been found to vary consistently from one speaker to another. Feature coefficients are chosen according to their Fisher Ratio. Compared with conventional Frequency Cepstrum Coefficient, the proposed feature vector can give greater distance between classes under the condition of same dimensions. A text-independent speaker recognition system has been complemented based on Vector Quantization to design the code-books of a given reference speaker. Experiment results demonstrate that the proposed feature vector has many good performances as compact, easy to be discriminated and low redundancy." @default.
- W2385623829 created "2016-06-24" @default.
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- W2385623829 date "2002-01-01" @default.
- W2385623829 modified "2023-09-25" @default.
- W2385623829 title "Research of Speaker Recognition Based on PCA" @default.
- W2385623829 hasPublicationYear "2002" @default.
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