ROBUST HYBRID FEATURES BASED TEXT INDEPENDENT SPEAKER IDENTIFICATION SYSTEM OVER NOISY ADDITIVE CHANNEL
Robustness of speaker identification systems over additive noise is crucial for real-world applications. In this paper, two robust features named Power Normalized Cepstral Coefficients (PNCC) and Gammatone Frequency Cepstral Coefficients (GFCC) are combined together to improve the robustness of spe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Mustansiriyah University/College of Engineering
2020-07-01
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| Series: | Journal of Engineering and Sustainable Development |
| Subjects: | |
| Online Access: | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/117 |
| Summary: | Robustness of speaker identification systems over additive noise is crucial for real-world applications. In this paper, two robust features named Power Normalized Cepstral Coefficients (PNCC) and Gammatone Frequency Cepstral Coefficients (GFCC) are combined together to improve the robustness of speaker identification system over different types of noise. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used as a feature matching and a classifier to identify the claim speakers. Evaluation results show that the proposed hybrid feature improves the performance of identification system when compared to conventional features over most types of noise and different signal-to-noise ratios.
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| ISSN: | 2520-0917 2520-0925 |