activity
20182024
most citedGeneralizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data

19 citations · 27 across the 6 of their papers we have counts for

collaborators

5 papers

cs.SD202119 cited

Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data

Pablo Gimeno, Victoria Mingote, Alfonso Ortega +2

Area under the ROC curve (AUC) optimisation techniques developed for neural networks have recently demonstrated their capabilities in different audio and speech related tasks. Howe…

eess.AS2019

Speech Enhancement with Wide Residual Networks in Reverberant Environments

Jorge Llombart, Dayana Ribas, Antonio Miguel +3

This paper proposes a speech enhancement method which exploits the high potential of residual connections in a Wide Residual Network architecture. This is supported on single dimen…

cs.SD2019

Optimization of the Area Under the ROC Curve using Neural Network Supervectors for Text-Dependent Speaker Verification

Victoria Mingote, Antonio Miguel, Alfonso Ortega +1

This paper explores two techniques to improve the performance of text-dependent speaker verification systems based on deep neural networks. Firstly, we propose a general alignment…

eess.AS20188 cited

Tied Hidden Factors in Neural Networks for End-to-End Speaker Recognition

Antonio Miguel, Jorge Llombart, Alfonso Ortega +1

In this paper we propose a method to model speaker and session variability and able to generate likelihood ratios using neural networks in an end-to-end phrase dependent speaker ve…

cs.SD2018

Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification

Victoria Mingote, Antonio Miguel, Alfonso Ortega +1

In this paper, we propose a new differentiable neural network alignment mechanism for text-dependent speaker verification which uses alignment models to produce a supervector repre…