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This paper presents a study on discriminative artificial neural networkclassifiers in the context of open-set speaker identification. Both 2-class andmulti-class architectures are tested against the conventional Gaussian mixturemodel based classifier on enrolled speaker sets of different sizes. Theperformance evaluation shows that the multi-class neural network system hassuperior performance for large population sizes.

Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks