Search Results for author:
Found 37 papers, 21 papers with code
Speaker anonymization using neural audio codec language models
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform is resynthesized using a vocoder.
Fairness and Privacy in Voice Biometrics:A Study of Gender Influences Using wav2vec 2.0
This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems.
Vocoder drift compensation by x-vector alignment in speaker anonymisation
For the most popular x-vector-based approaches to speaker anonymisation, the bulk of the anonymisation can stem from vocoding rather than from the core anonymisation function which is used to substitute an original speaker x-vector with that of a fictitious pseudo-speaker.
Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics
In this paper, we present an adversarial Auto-Encoder--based approach to hide gender-related information in speaker embeddings, while preserving their effectiveness for speaker verification.
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems
We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs).
Vocoder drift in x-vector-based speaker anonymization
State-of-the-art approaches to speaker anonymization typically employ some form of perturbation function to conceal speaker information contained within an x-vector embedding, then resynthesize utterances in the voice of a new pseudo-speaker using a vocoder.
Towards single integrated spoofing-aware speaker verification embeddings
Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge.
Can spoofing countermeasure and speaker verification systems be jointly optimised?
Spoofing countermeasure (CM) and automatic speaker verification (ASV) sub-systems can be used in tandem with a backend classifier as a solution to the spoofing aware speaker verification (SASV) task.
On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification
The spoofing-aware speaker verification (SASV) challenge was designed to promote the study of jointly-optimised solutions to accomplish the traditionally separately-optimised tasks of spoofing detection and speaker verification.
The VoicePrivacy 2020 Challenge Evaluation Plan
The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges.
The VoicePrivacy 2022 Challenge Evaluation Plan
Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers.
Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
Despite several years of research in deepfake and spoofing detection for automatic speaker verification, little is known about the artefacts that classifiers use to distinguish between bona fide and spoofed utterances.
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation
The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data.
RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing
This paper introduces RawBoost, a data boosting and augmentation method for the design of more reliable spoofing detection solutions which operate directly upon raw waveform inputs.
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations
Substantial progress in spoofing and deepfake detection has been made in recent years.
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and the development of countermeasures.
The VoicePrivacy 2020 Challenge: Results and findings
We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results.
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection.
End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection
Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments.
Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection
End-to-end approaches to anti-spoofing, especially those which operate directly upon the raw signal, are starting to be competitive with their more traditional counterparts.
Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity.
Graph Attention Networks for Anti-Spoofing
This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance.
Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection
This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems.
ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV).
End-to-end anti-spoofing with RawNet2
Spoofing countermeasures aim to protect automatic speaker verification systems from attempts to manipulate their reliability with the use of spoofed speech signals.
Speaker anonymisation using the McAdams coefficient
Anonymisation has the goal of manipulating speech signals in order to degrade the reliability of automatic approaches to speaker recognition, while preserving other aspects of speech, such as those relating to intelligibility and naturalness.
Texture-based Presentation Attack Detection for Automatic Speaker Verification
Despite these and other advantages, biometric systems in general and Automatic speaker verification (ASV) systems in particular can be vulnerable to attack presentations.
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs.
The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment
Mounting privacy legislation calls for the preservation of privacy in speech technology, though solutions are gravely lacking.
Cryptography and Security Audio and Speech Processing
Introducing the VoicePrivacy Initiative
The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Speaker Anonymization Using X-vector and Neural Waveform Models
One solution to mitigate these concerns involves the concealing of speaker identities before the sharing of speech data.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Introduction to Voice Presentation Attack Detection and Recent Advances
Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV).
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric.
EMOVO Corpus: an Italian Emotional Speech Database
It is observed that emotions less easy to recognize are joy and disgust, whereas the most easy to detect are anger, sadness and the neutral state.
Intelligibility assessment in forensic applications
In the absence of the original signal, the only way to see the level of accuracy that can be obtained in the transcription of poor recordings is to develop an objective methodology for intelligibility measurements.
