SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
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This paper describes the design and implementation of a practical automatic speaker recognition system for the CSLP speaker recognition evaluation (SRE). The speaker recognition system is built upon four subsystems using speaker information from acoustic spectral features. In addition to the conventional spectral features, a novel temporal discrete cosine transform (TDCT) feature is introduced in order to capture long-term speech dynamic. The speaker information is modeled using two complementary speaker modeling techniques, namely, Gaussian mixture model (GMM) and support vector machine (SVM). The resulting subsystems are then integrated at the score level through a multilayer perceptron (MLP) neural network. Evaluation results confirm that the feature selection, classifier design, and fusion strategy are successful, giving rise to an effective speaker recognition system.