Designing Highly Secured Speaker Identification with Audio Fingerprinting using MODWT and RBFNN

Authors

  • I. Rajani Kumari Research Scholar of GITAM UNIVERSITY, Department of ECE, Vizag, AP, INDIA, & Assistant Professor of Geethanjali college of engineering and technology, Department of ECE,Hyderabad,INDIA
  • Kalyan Babu Professor, Departmentof ECE,GITAM ,Vizag,AP,INDIA

Keywords:

RBFNN-PNCC, HMM-LSTM, MODWT, Speaker identification, Audio fingerprinting

Abstract

The process of matching Speech data with database entries is known as Speaker identification. This research processes a Novel technique to speaker recognition by merging neural networks with audio fingerprinting. In comparison to classical models the radial basis function neural network(RBFNN) combined with the hidden markov model(HMM) provides superior results. To facilitate the denoising process for both spoken and unvoiced speech, including ambient noise, the speech data is divided into low and high frequency segements.The maximal overlap discrete wavelet transform (MODWT) is used to denoise the split signals. When it comes to boundaries MODWT is more resilient. In the presence of white noise the Power normalized cepstral coefficients (PNCC) will provide more accurate results than Mel cepstrums.The results are accurately observed using this suggested strategy in relation to SNR.

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Published

23.02.2024

How to Cite

Kumari, I. R. ., & Babu, K. . (2024). Designing Highly Secured Speaker Identification with Audio Fingerprinting using MODWT and RBFNN . International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 25–30. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4779

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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