19 citations · 28 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems
Victoria Mingote, Antonio Miguel, Alfonso Ortega +1
This paper explores three novel approaches to improve the performance of speaker verification (SV) systems based on deep neural networks (DNN) using Multi-head Self-Attention (MSA)…
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions
Santi Prieto, Alfonso Ortega, Iván López-Espejo +1
The performance of speaker verification systems degrades when vocal effort conditions between enrollment and test (e.g., shouted vs. normal speech) are different. This is a potenti…
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…
Progressive Speech Enhancement with Residual Connections
Jorge Llombart, Dayana Ribas, Antonio Miguel +3
This paper studies the Speech Enhancement based on Deep Neural Networks. The proposed architecture gradually follows the signal transformation during enhancement by means of a visu…
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…