19 citations · 28 across the 14 of their papers we have counts for
15 papers
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…
Audio-Visual Speaker Diarization: Current Databases, Approaches and Challenges
Victoria Mingote, Alfonso Ortega, Antonio Miguel +1
Nowadays, the large amount of audio-visual content available has fostered the need to develop new robust automatic speaker diarization systems to analyse and characterise it. This…
Rethinking Disentanglement under Dependent Factors of Variation
Antonio Almudévar, Alfonso Ortega
Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it se…
Predefined Prototypes for Intra-Class Separation and Disentanglement
Antonio Almudévar, Théo Mariotte, Alfonso Ortega +4
Prototypical Learning is based on the idea that there is a point (which we call prototype) around which the embeddings of a class are clustered. It has shown promising results in s…
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)…
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…
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…
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…
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…
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…