Abstract
Automatic Speaker Verification (ASV) is a highly unbalanced binary classification problem, in which any given speaker must be verified against everyone else. We apply Genetic programming (GP) to this problem with the aim of both prediction and inference. We examine the generalisation of evolved programs using a variety of fitness functions and data sampling techniques found in the literature. A significant difference between train and test performance, which can indicate overfitting, is found in the evolutionary runs of all to-be-verified speakers. Nevertheless, in all speakers, the best test performance attained is always superior than just merely predicting the majority class. We examine which features are used in good-generalising individuals. The findings can inform future applications of GP or other machine learning techniques to ASV about the suitability of feature-extraction techniques.
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Notes
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\(I(\cdot )\) is the indicator function.
- 3.
The number of examples correctly classified as a fraction of the total number of training examples.
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Acknowledgments
This work was carried out as a collaboration of projects funded by Science Foundation Ireland under grant Grant Numbers 08/SRC/FM1389 and 13/IA/1850.
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Loughran, R., Agapitos, A., Kattan, A., Brabazon, A., O’Neill, M. (2016). Speaker Verification on Unbalanced Data with Genetic Programming. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_47
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