Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-31204-0_47 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Speaker verification,Unbalanced data,Genetic programming,Feature selection | Speaker verification,Feature selection,Binary classification,Inference,Generalization,Computer science,Genetic programming,Artificial intelligence,Overfitting,Data sampling,Machine learning | Conference |
Volume | ISSN | Citations |
9597 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Róisín Loughran | 1 | 21 | 3.63 |
| Alexandros Agapitos | 2 | 211 | 22.88 |
| Ahmed Kattan | 3 | 96 | 13.23 |
| Anthony Brabazon | 4 | 918 | 98.60 |
| Michael O'Neill | 5 | 15 | 2.17 |