8 citations · 22 across the 14 of their papers we have counts for
15 papers
Evaluating Machine Unlearning in ASR
Diogo Dinis, Francisco Teixeira, Bhiksha Raj +2
Machine unlearning (MU) offers a path to compliance with "right to be forgotten" regulations. While MU has received increasing attention for speech tasks, it remains largely unexpl…
ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era
Masao Someki, Alexander Polok, Carlos Carvalho +14
Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort…
FalAR: A Large-scale Speaker-Annotated European Portuguese Speech Corpus of Parliamentary Sessions
Francisco Teixeira, Carlos Carvalho, Mariana Julião +7
State-of-the-art performance for Automatic Speech Recognition (ASR) largely depends on the availability of large-scale labeled corpora. This creates a demand for increased data col…
Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis
Carlos Franzreb, Francisco Teixeira, Ben Luks +2
Automatic detection of Parkinson's disease (PD) from speech is a promising non-invasive diagnostic tool, but it raises significant privacy concerns. Speaker anonymization mitigates…
Exploring the potential and limitations of Model Merging for Multi-Domain Adaptation in ASR
Carlos Carvalho, Francisco Teixeira, Thomas Rolland +1
Model merging is a scalable alternative to multi-task training that combines the capabilities of multiple specialised models into a single model. This is particularly attractive fo…
SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions
Massa Baali, Sarthak Bisht, Francisco Teixeira +3
Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems, yet their robustness to many real-world challenges remains…
CAMÕES: A Comprehensive Automatic Speech Recognition Benchmark for European Portuguese
Carlos Carvalho, Francisco Teixeira, Catarina Botelho +9
Existing resources for Automatic Speech Recognition in Portuguese are mostly focused on Brazilian Portuguese, leaving European Portuguese (EP) and other varieties under-explored. T…
Children's Voice Privacy: First Steps And Emerging Challenges
Ajinkya Kulkarni, Francisco Teixeira, Enno Hermann +3
Children are one of the most under-represented groups in speech technologies, as well as one of the most vulnerable in terms of privacy. Despite this, anonymization techniques targ…
Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge
Catarina Botelho, David Gimeno-Gómez, Francisco Teixeira +11
This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks…
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features
Francisco Teixeira, Karla Pizzi, Raphael Olivier +3
Membership Inference (MI) poses a substantial privacy threat to the training data of Automatic Speech Recognition (ASR) systems, while also offering an opportunity to audit these m…
Privacy-oriented manipulation of speaker representations
Francisco Teixeira, Alberto Abad, Bhiksha Raj +1
Speaker embeddings are ubiquitous, with applications ranging from speaker recognition and diarization to speech synthesis and voice anonymisation. The amount of information held by…
Privacy-preserving Automatic Speaker Diarization
Francisco Teixeira, Alberto Abad, Bhiksha Raj +1
Automatic Speaker Diarization (ASD) is an enabling technology with numerous applications, which deals with recordings of multiple speakers, raising special concerns in terms of pri…
Towards End-to-End Private Automatic Speaker Recognition
Francisco Teixeira, Alberto Abad, Bhiksha Raj +1
The development of privacy-preserving automatic speaker verification systems has been the focus of a number of studies with the intent of allowing users to authenticate themselves…
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
Ali Shahin Shamsabadi, Francisco Sepúlveda Teixeira, Alberto Abad +3
Speaker identification models are vulnerable to carefully designed adversarial perturbations of their input signals that induce misclassification. In this work, we propose a white-…
Pathological speech detection using x-vector embeddings
Catarina Botelho, Francisco Teixeira, Thomas Rolland +2
The potential of speech as a non-invasive biomarker to assess a speaker's health has been repeatedly supported by the results of multiple works, for both physical and psychological…