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Speaker identification based on normalized pitch frequency and Mel Frequency Cepstral Coefficients

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This paper presents an efficient approach for automatic speaker identification based on cepstral features and the Normalized Pitch Frequency (NPF). Most relevant speaker identification methods adopt a cepstral strategy. Inclusion of the pitch frequency as a new feature in the speaker identification process is expected to enhance the speaker identification accuracy. In the proposed framework for speaker identification, a neural classifier with a single hidden layer is used. Different transform domains are investigated for reliable feature extraction from the speech signal. Moreover, a pre-processing noise reduction step, is used prior to the feature extraction process to enhance the performance of the speaker identification system. Simulation results prove that the NPF as a feature in speaker identification enhances the performance of the speaker identification system, especially with the Discrete Cosine Transform (DCT) and wavelet denoising pre-processing step.

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Correspondence to Marwa A. Nasr.

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Nasr, M.A., Abd-Elnaby, M., El-Fishawy, A.S. et al. Speaker identification based on normalized pitch frequency and Mel Frequency Cepstral Coefficients. Int J Speech Technol 21, 941–951 (2018).

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