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20182026
most citedGeneralizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data

19 citations · 27 across the 9 of their papers we have counts for

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6 papers · 1 filter

eess.AS2026

An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures

Santiago Rubio, Pilar Bello, Dayana Ribas +3

While deepfake audio detection systems achieve high performance in controlled benchmarks, their reliability often diminishes in the wild. Prior work shows that dataset-specific art…

eess.AS2026

Open-Set Source Tracing as Compositional Factors via Structured Prototypes

Santiago Rubio, Antonio Almudévar, Antonio Miguel +2

Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of synthetic speech. However, curr…

eess.AS2026

A Fair and Transparent Framework for Speech-Based Depression Detection: Balancing Interpretability and Performance

Mariel Estevez, Alfonso Ortega, Antonio Miguel +1

While speech provides rich, non-invasive biomarkers for mental-health assessment, clinical adoption is limited by opaque models and potential demographic bias. In this work we prop…

eess.AS2023

Improved Vocal Effort Transfer Vector Estimation for Vocal Effort-Robust Speaker Verification

Iván López-Espejo, Santi Prieto, Alfonso Ortega +1

Despite the maturity of modern speaker verification technology, its performance still significantly degrades when facing non-neutrally-phonated (e.g., shouted and whispered) speech…

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…

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…