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Analysing the Performance of Speaker Verification Task using Different Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 10 |
| Year of Publication: 2013 |
| Authors: L. Kavitha, B. Bharathi |
10.5120/13144-0550
|
L. Kavitha, B. Bharathi . Analysing the Performance of Speaker Verification Task using Different Features. International Journal of Computer Applications. 75, 10 ( August 2013), 1-5. DOI=10.5120/13144-0550
Abstract
Speaker recognition is the identification of the person who is speaking by characteristics of their voices, also called "voice recognition". The components of Speaker Recognition includes Speaker Identification(SI) and Speaker Verification(SV). Speaker identification is the task of determining an unknown speakers identity. If the speaker claims to be of a certain identity and the voice is to verify this claim, this is called Speaker Verification. It determines whether an unknown voice matches the known voice of a speaker whose identity is being claimed. This paper proposes Speaker Verification task. There are two phases in the Speaker Verification task namely, training and testing. In the training phase, different features such as Mel Frequency Cepstral Coefficient(MFCC), Linear Predictive Cepstral Coefficient(LPCC), Perceptual Linear Predictive(PLP) are extracted from the speech signal and is trained by Support Vector Machine to get the target speaker model. It is trained with both actual speaker and impostor utterances. In the testing phase, features are extracted from the test speech signal . The test features are extracted for different duration of time. The extracted feature vectors are given to the claimed speaker model and the decision is taken as authorised speaker or an impostor. The performance of a speaker verification task is analysed using different features with different utterance sizes. The result shows that the performance of a speaker verification task decreases when the duration of the speech utterances decreased.
References
- Ms. Arundhati S. Mehendale and Mrs. M. R. Dixit "Speaker Identification", An International journal in Signal and Image Processing, Vol. 2, June 2011.
- Amin Fazel and Shantanu Chakrabartty " An Overview of Statistical pattern Recognition Techniques for Speaker Verification". IEEE Circuits and Systems Magazine 2011.
- Douglas A. Reynolds Thomas F. Quatieri and Robert B. Dunn "Speaker Verification using Adapted Gaussian Mixture Models", Digital Signal Processing, Vol. 10, Nos. 1-3, January 2000
- Vibha Tiwari "MFCC and its applications in Speaker recognition", International Journal on Emerging Technologies 1(1): 19-22(2010)
- Hynek Hermansky "Perceptual Linear Predictive(PLP) analysis of Speech", Journal of Acoustical Society of America, Vol. 87, No. 4:1738-1752 November 1989
- Petr Motlicek Vijay Ullal and Hynek Hermansky "Wide- Band Perceptual Audio Coding based on Frequency- Domain Linear Prediction", IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 1 I-265 - I-268, 2007
- Raghavan. S, G. Lazarou and J. Picone "Speaker Verification using Support Vector Machine", IEEE Transaction on Computers, 2006
- Joseph P. Campbell "Speaker Verification: A tutorial", Vol. 85, No. 9 September 1997.
- Shi-Huang Chen and Yu-Ren Luo "Speaker Verification Using MFCC and Support Vector Machine", International Multiconference of Engineers and Computer Scientists, 2009 5
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