138 citations · 299 across the 17 of their papers we have counts for
18 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…
Permutation Invariant Recurrent Neural Networks for Sound Source Tracking Applications
David Diaz-Guerra, Archontis Politis, Antonio Miguel +2
Many multi-source localization and tracking models based on neural networks use one or several recurrent layers at their final stages to track the movement of the sources. Conventi…
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…
Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks
David Diaz-Guerra, Antonio Miguel, Jose R. Beltran
In this paper, we present a new single sound source DOA estimation and tracking system based on the well-known SRP-PHAT algorithm and a three-dimensional Convolutional Neural Netwo…
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…
Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks
Dayana Ribas, Jorge Llombart, Antonio Miguel +1
This paper proposes a deep speech enhancement method which exploits the high potential of residual connections in a wide neural network architecture, a topology known as Wide Resid…
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…
Disentangling and Learning Robust Representations with Natural Clustering
Javier Antoran, Antonio Miguel
Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the s…
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…
gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration
David Diaz-Guerra, Antonio Miguel, Jose R. Beltran
The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the…