An investigation into the reliability of speaker recognition schemes: analysing the impact of environmental factors utilising deep learning techniques

This paper studies the performance and reliability of deep learning-based speaker recognition schemes under various recording situations and background noise presence. The study uses the Speaker Recognition Dataset offered in the Kaggle website, involving audio recordings from different speakers, and four scenarios with various combinations of speakers. In the first scenario, the scheme achieves discriminating capability and high accuracy in identifying speakers without taking into account outside noise, having roughly one area under the ROC curve. Nevertheless, in the second scenario, with background noise added to the recording, accuracy decreases, and misclassifications increase. However, the scheme still reveals good discriminating power, with ROC areas ranging from 0.77 to 1.