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

Stefano Imoscopi, Volodya Grancharov, Sigurdur Sverrisson, Erlendur Karlsson, Harald Pobloth
2019 arXiv   pre-print
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