Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database
In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed fo...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-05-01.
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| Online Access: | Get fulltext |
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| LEADER | 02739 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs20106_13706 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Al-Kaltakchi, Musab T. S. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Al-Raheem Taha, Haithem Abd |e author |
| 700 | 1 | 0 | |a Abd Shehab, Mohanad |e author |
| 700 | 1 | 0 | |a Abdullah, Mohamed A.M. |e author |
| 245 | 0 | 0 | |a Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-05-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20106 | ||
| 520 | |a In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a pattern recognition; signal processing; | ||
| 690 | |a Cepstral mean variance normalization (CMVN); Coefficients (MFCCS); Gaussian mixture model (GMM); Mel frequency cepstral; Power normalized cepstral coefficients (PNCCS); Speaker recognition | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 2: May 2020; 782-789 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20106/13706 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20106/13706 |z Get fulltext |