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Cross B-HUB Based RNN with Random Aural-Feature Extraction for Enhanced Speaker Extraction and Speaker Recognition

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Abstract

Speaker recognition is the identification of a person from characteristics of voices hence various robust training and classification algorithms for automatic voice recognition has been presented previously but they had performance degradation due to contaminated acoustic noise in the actual world. Hence, a novel Random-Aural Feature Extraction had presented to extract robust acoustic features from voice signals in which Discrete Cosine Transform (DCT) and Discrete Fourier Transform is applied to the log of the filter bank output in order to amplify stochastic acoustic characteristics and improve feature extraction accuracy. But while performing optimization for feature selection and classification, the issue of long-term dependency develops. Therefore, the Cross B-HUB based RNN network is proposed which creates the opposition based initialization and reduce computational overhead by the Cross B-HUB algorithm. Then, the classification is done by RNN based classification that performs verification and identification of the speaker with eliminating the issues in long term dependencies. The proposed approach has been found to have the highest sensitivity, specificity, and accuracy of 97, 99, and 97%, respectively.

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Authors and Affiliations

  1. Electronics and Communication Engineering, RVR & JC College of Engineering (Autonomous), Chowdavaram, Guntur, India

    P. S. Subhashini Pedalanka & Manchikalapudi Satya Sai Ram

  2. Electronics and Communication Engineering, JNTUH College of Engineering, Kukatpally, Hyderabad, India

    Duggirala Sreenivasa Rao

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  1. P. S. Subhashini Pedalanka
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  2. Manchikalapudi Satya Sai Ram
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Correspondence to P. S. Subhashini Pedalanka.

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Pedalanka, P.S.S., Ram, M.S.S. & Rao, D.S. Cross B-HUB Based RNN with Random Aural-Feature Extraction for Enhanced Speaker Extraction and Speaker Recognition. Wireless Pers Commun 129, 2239–2268 (2023). https://doi.org/10.1007/s11277-022-10096-3

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