SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Short-time fractional fourier transform and its applications
IEEE Transactions on Signal Processing
Comparison of the impact of some Minkowski metrics on VQ/GMM based speaker recognition
Computers and Electrical Engineering
The discrete fractional Fourier transform
IEEE Transactions on Signal Processing
The fractional Fourier transform and time-frequency representations
IEEE Transactions on Signal Processing
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Robust Speaker Recognition in Noisy Conditions
IEEE Transactions on Audio, Speech, and Language Processing
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This paper presents a text-independent speaker recognition technique in which the conventional Fourier transform in Mel-Frequency Cepstral Coefficient (MFCC) front-end is substituted by fractional Fourier transform. Support Vector Machine (SVM) maps these input features into a high-dimensional space to separate classes by a hyperplane with enhanced discrimination capability. SVM based on mean-squared error classifier produces more accurate system. The Fractional Fourier Transform (FrFT) reveals the mixed time and frequency components of the signal. Modelling of speech signals as mixed time and frequency signals represents better production and perception speech characteristics. Processing of time-varying signals in fractional Fourier domain allows us to estimate the signal with least Mean Square Error (MSE) making the technique robust against additive noise compared to Fourier domain maintaining same computational complexity. The feasibility of the proposed technique has been tested experimentally using Texas Instruments and Massachusetts Institute of Technology (TIMIT) and Shri Guru Gobind Singhji (SGGS) databases. The experimental results show the superiority of the proposed method.