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Real time implementation of voice based robust person authentication using T-F features and CNN

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Abstract

A forensic investigation uses personal traits to identify the persons involved in criminal offences. In this work on person authentication, the recorded voice samples can also be used to narrow down the search to identify persons. Time-frequency (T-F) features obtained from the concatenated training set of utterances are given to the convolutional neural networks (CNN), with layers configured for creating templates. Testing utterances are tied, and T-F features are derived. These features are applied to the CNN templates, and based on the match claimed, recognition accuracy is computed to validate the feature selection and CNN technique. Decision-level fusion of features with CNN for modelling and classification provides an overall authentication rate of 98%. This system is also implemented in real-time using Raspberry Pi hardware. This automated system would be helpful in identifying convicts in forensic sectors and perform secured online transactions against fraudulent attacks in financial sectors.

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Acknowledgements

Authors wish to express their sincere thanks to the SASTRA Deemed University, Thanjavur, India, for extending infrastructural support to carry out this work.

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Authors and Affiliations

  1. Department of ECE/SEEE, SASTRA Deemed University, Thanjavur, India

    A. Revathi & N. Raju

  2. Department of CSE/SEEE, SASTRA Deemed University, Thanjavur, India

    N. Sasikaladevi

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  1. A. Revathi
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  3. N. Raju
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Correspondence to A. Revathi.

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Revathi, A., Sasikaladevi, N. & Raju, N. Real time implementation of voice based robust person authentication using T-F features and CNN. Multimed Tools Appl 83, 31587–31601 (2024). https://doi.org/10.1007/s11042-023-16811-x

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  • DOI: https://doi.org/10.1007/s11042-023-16811-x

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