Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks
This work addresses speaker identification for security or authentication systems, but it is incremental as it compares existing methods on standard tasks.
The paper tackled open-set speaker identification by testing discriminative neural networks against Gaussian mixture models, finding that multi-class neural networks perform better for large speaker populations.
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.