Publications

@article{carofilis-vasco_improvement_2023,

title = {Improvement of accent classification models through Grad-Transfer from Spectrograms and Gradient-weighted Class Activation Mapping},

author = {Andrés Carofilis-Vasco and Enrique Alegre and Eduardo Fidalgo and Laura Fernández-Robles},

url = {https://ieeexplore.ieee.org/abstract/document/10190103},

year  = {2023},

date = {2023-01-01},

journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},

abstract = {This article introduces a new method for accent classification using a descriptor called Grad-Transfer, which is extracted using Gradient-weighted Class Activation Mapping (Grad-CAM) based on convolutional neural network (CNN) interpretability. The proposed methodology transfers the knowledge gained by CNNs to classical machine learning algorithms. The study shows that Grad-CAM highlights key regions of spectrograms important for accent prediction, and the generated Grad-Transfer descriptors effectively distinguish different accents. Experiments on the Voice Cloning Toolkit dataset demonstrate an improvement in accent classification accuracy and recall when using Grad-Transfer, outperforming models trained directly on spectrograms.},

note = {Publisher: IEEE},

keywords = {Accent Classification, deep learning, Grad-Transfer, machine learning},

pubstate = {published},

tppubtype = {article}

}