Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database
| Autor: | Haithem Abd Al-Raheem Taha, Mohanad Abd Shehab, Mohamed A.M. Abdullah, Musab T. S. Al-Kaltakchi |
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| Rok vydání: | 2020 |
| Předmět: |
Normalization (statistics)
Control and Optimization Database Power normalized cepstral coefficients (PNCCS) Computer Networks and Communications Computer science Feature extraction Grid Speaker recognition computer.software_genre Mixture model Cepstral mean variance normalization (CMVN) Coefficients (MFCCS) Gaussian mixture model (GMM) Hardware and Architecture Mel frequency cepstral Signal Processing Cepstrum Mel-frequency cepstrum Electrical and Electronic Engineering Image warping computer Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 18:782 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v18.i2.pp782-789 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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