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Combining evidence from residual phase and MFCC features for speaker recognition
Abstract
The objective of this letter is to demonstrate the complementary nature of speaker-specific information present in the residual phase in comparison with the information present in the conventional mel-frequency cepstral coefficients (MFCCs). The residual phase is derived from speech signal by linear prediction analysis. Speaker recognition studies are conducted on the NIST-2003 database using the proposed residual phase and the existing MFCC features. The speaker recognition system based on the residual phase gives an equal error rate (EER) of 22%, and the system using the MFCC features gives an EER of 14%. By combining the evidence from both the residual phase and the MFCC features, an EER of 10.5% is obtained, indicating that speaker-specific excitation information is present in the residual phase. This information is useful since it is complementary to that of MFCCs.
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Cited by
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Karan, B., Sahu, S. S., & Mahto, K. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2019.05.005 · Cited by 165
“Combating replay attacks against voice assistants,”
Pradhan, S., Sun, W., Baig, G., & Qiu, L. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3351258 · Cited by 67
Acoustic Features Analysis for Explainable Machine Learning-Based Audio Spoofing Detection
Bisogni, C., Loia, V., Nappi, M., & Pero, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104145 · Cited by 30
Metadata
- Authors
- Murty, K. S. R., & Yegnanarayana, B.
- Venue
- IEEE Signal Processing Letters
- Published
- 2006-01
- Volume
- 13
- Issue
- 1
- Pages
- 52-55
- ISSN
- 1070-9908 (ISSN-L)
- OpenAlex
- W2103075368
- Cited by
- 416
- References
- 0
- FWCI
- 9.96
- Type
- journal article
- Language
- en
- Open access
- closed
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