International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering | |
Gammatone Cepstral Coefficient forSpeaker Identification | |
article | |
Rahana Fathima1  Raseena P E1  | |
[1] Ilahia college of Engineering and Technology | |
关键词: Feature extraction; Feature matching; Gammatone Cepstral coefficient; Speaker identification; | |
来源: Research & Reviews | |
【 摘 要 】
Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Taking as a basis Mel frequency cepstral coefficients (MFCC) used for speaker identification and audio parameterization, the Gammatone cepstral coefficients (GTCCs) are a biologically inspired modification employing Gammatone filters with equivalent rectangular bandwidth bands. A comparison is done between MFCC and GTCC for speaker identification.Thier performance is evaluated using three machine learning methods neural network (NN) and support vector machine (SVM) and K-nearest neighbor (KNN). According to the results, classification accuracies are significantly higher when employing GTCC in speaker identification than MFCC.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO202307140000094ZK.pdf | 291KB | download |