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Text-dependent and text-independent speaker recognition of reverberant speech based on CNN

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Abstract

Speaker recognition is one of several biometric recognition systems owing to its high importance in numerous applications of security and telecommunications. The key aspiration of speaker recognition systems is to know who is speaking depending on voice characteristics. This paper presents an extensive study of speaker recognition in both text-dependent and text-independent cases. Convolutional Neural Network (CNN) based feature extraction is extended to the text-dependent and text-independent speaker recognition tasks. In addition, the effect of reverberation on the speaker recognition system is addressed. All speech signals are converted into images by obtaining their spectrograms. Two proposed CNN models are presented for efficient speaker recognition from clean and reverberant speech signals. They depend on image processing concepts applied on spectrograms of speech signals. One of the proposed models is compared with a conventional Benchmark model in the text-independent scenario. The performance of the recognition system is measured by the recognition rate in the cases of clean and reverberant speech.

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Authors and Affiliations

  1. Communications and Electronics Department, Tanta High Institute of Engineering and Technology, Tanta, Egypt

    Samia Abd El-Moneim

  2. Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligents, Kafrelsheikh University, Kafr Al Sheikh, Egypt

    Ahmed Sedik

  3. Department of Electronics and Electrical Communications and Electronics, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    M. A. Nassar, Adel S. El-Fishawy, A. M. Sharshar, Shaimaa E. A. Hassan, Moawd I. Dessouky, Fathi E. Abd El-Samie, El-Sayed M. El-Rabaie & G. S. M. Elabyad

  4. Electronics and Communications Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt

    Adel Zaghloul Mahmoud

  5. Automatic Control Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt

    Ghada M. El-Banby

  6. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

  7. Productivity and Vocational Training Department, Ministry of Industry, Cairo, Egypt

    Badawi Neyazi

  8. Acoustic Laboratory, Housing and Building National Research Center, Giza, Egypt

    H. S. Seddeq

  9. Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Nabil A. Ismail

  10. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Ashraf A. M. Khalaf

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  1. Samia Abd El-Moneim
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  2. Ahmed Sedik
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  3. M. A. Nassar
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  4. Adel S. El-Fishawy
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  5. A. M. Sharshar
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  6. Shaimaa E. A. Hassan
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  7. Adel Zaghloul Mahmoud
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  8. Moawd I. Dessouky
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  9. Ghada M. El-Banby
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  10. Fathi E. Abd El-Samie
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  11. El-Sayed M. El-Rabaie
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  12. Badawi Neyazi
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  13. H. S. Seddeq
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  14. Nabil A. Ismail
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  15. Ashraf A. M. Khalaf
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  16. G. S. M. Elabyad
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Correspondence to Fathi E. Abd El-Samie.

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El-Moneim, S.A., Sedik, A., Nassar, M.A. et al. Text-dependent and text-independent speaker recognition of reverberant speech based on CNN. Int J Speech Technol 24, 993–1006 (2021). https://doi.org/10.1007/s10772-021-09805-3

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  • DOI: https://doi.org/10.1007/s10772-021-09805-3

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