Deep Learning-Based End-to-End Speaker Identification Using Time–Frequency Representation of Speech Signal
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s00034-023-02542-9.pdf
Reference53 articles.
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2. N.N. An, N.Q. Thanh, Y. Liu, Deep CNNs with self-attention for speaker identification. IEEE Access 7, 85327–85337 (2019). https://doi.org/10.1109/ACCESS.2019.2917470
3. T. Arias-Vergara, P. Klumpp, J.C. Vasquez-Correa, E. Nöth, J.R. Orozco-Arroyave, M. Schuster, Multi-channel spectrograms for speech processing applications using deep learning methods. Pattern Anal. Appl. 24(2), 423–431 (2021). https://doi.org/10.1007/s10044-020-00921-5
4. A. Ashar, M.S. Bhatti, U. Mushtaq, Speaker identification using a hybrid CNN-MFCC approach, in 2020 International Conference on Emerging Trends in Smart Technologies ICETST 2020, 2020. https://doi.org/10.1109/ICETST49965.2020.9080730
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