MFCC as Features for Speaker Classification using Machine Learning
Author:
Affiliation:
1. University of St. Thomas,Department of Electrical and Computer Engineering,St. Paul,MN,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10174256/10174258/10174566.pdf?arnumber=10174566
Reference10 articles.
1. Automatic Classification of Bird Sounds: Using MFCC and Mel Spectrogram Features with Deep Learning
2. Deepfake Audio Detection via MFCC Features Using Machine Learning
3. Phone Call Speaker Classification using Machine Learning on MFCC Features for Scam Detection
4. Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task;ganchev;10th International Conference on Speech and Computer (SPECOM 2005),2005
5. Comparison of Different Implementations of MFCC;feng;Journal of Computer Science and Technology,2001
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2. Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs;PLOS ONE;2024-02-14
3. A Depression Detection Auxiliary Decision System Based on Multi-Modal Feature-Level Fusion of EEG and Speech;IEEE Transactions on Consumer Electronics;2024-02
4. Enhancing Biometric Speaker Recognition Through MFCC Feature Extraction and Polar Codes for Remote Application;IEEE Access;2023
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