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Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks
[article]
2019
arXiv
pre-print
Preserved Fulltext
This paper presents a study on discriminative artificial neural network classifiers in the context of open-set speaker identification. Both 2-class and multi-class architectures are tested against the conventional Gaussian mixture model based classifier on enrolled speaker sets of different sizes. The performance evaluation shows that the multi-class neural network system has superior performance for large population sizes.
arXiv:1904.01269v1
fatcat:mvihbz3xg5etxmpqzpplbn2yeq