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Found 31 papers, 11 papers with code
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models.
Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components
We use these probabilistic embeddings with four classifier back-ends to address two downstream tasks: spoofing detection and spoofing attack attribution.
SpoofCeleb: Speech Deepfake Detection and SASV In The Wild
This paper introduces SpoofCeleb, a dataset designed for Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV), utilizing source data from real-world conditions and spoofing attacks generated by Text-To-Speech (TTS) systems also trained on the same real-world data.
Text-To-Speech Synthesis In The Wild
The recent literature nonetheless shows efforts to train TTS systems using data collected in the wild.
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions.
Beyond Silence: Bias Analysis through Loss and Asymmetric Approach in Audio Anti-Spoofing
Our investigations highlight the significant differences in training dynamics between the two classes, emphasizing the need for future research to focus on robust modeling of the bonafide class.
To what extent can ASV systems naturally defend against spoofing attacks?
The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target.
a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
Spoofing detection is today a mainstream research topic.
Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing
Audio anti-spoofing for automatic speaker verification aims to safeguard users' identities from spoofing attacks.
How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning
Shortcut learning, or `Clever Hans effect` refers to situations where a learning agent (e. g., deep neural networks) learns spurious correlations present in data, resulting in biased models.
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.
Large-scale learning of generalised representations for speaker recognition
We also show that training with proposed large data configurations gives better performance.
Extended U-Net for Speaker Verification in Noisy Environments
Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility.
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions.
RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies
Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems.
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains.
Ranked #1 on
Voice Anti-spoofing
on ASVspoof 2019 - LA
Attentive max feature map and joint training for acoustic scene classification
Furthermore, adopting the proposed attentive max feature map, our team placed fourth in the recent DCASE 2021 challenge.
Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System
By learning the reliable intermediate representation of the mean teacher network, we expect that the proposed method can explore more discriminatory embedding spaces and improve the generalization performance of the speaker verification system.
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
Single task deep neural networks that perform a target task among diverse cross-related tasks in the acoustic scene and event literature are being developed.
Capturing scattered discriminative information using a deep architecture in acoustic scene classification
Various experiments are conducted using the detection and classification of acoustic scenes and events 2020 task1-a dataset to validate the proposed methods.
Integrated Replay Spoofing-aware Text-independent Speaker Verification
In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach.
Segment Aggregation for short utterances speaker verification using raw waveforms
The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding.
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.
A study on the role of subsidiary information in replay attack spoofing detection
For addition, we utilize the multi-task learning framework to include subsidiary information to the code.
Self-supervised pre-training with acoustic configurations for replay spoofing detection
Constructing a dataset for replay spoofing detection requires a physical process of playing an utterance and re-recording it, presenting a challenge to the collection of large-scale datasets.
Cosine similarity-based adversarial process
In particular, the adversarial process degrades the performance of the subsidiary model by eliminating the subsidiary information in the input which, in assumption, may degrade the performance of the primary model.
Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge
To detect unrevealed characteristics that reside in a replayed speech, we directly input spectrograms into an end-to-end DNN without knowledge-based intervention.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification.
End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification
Each speaker basis is designed to represent the corresponding speaker in the process of training deep neural networks.
Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
The short duration of an input utterance is one of the most critical threats that degrade the performance of speaker verification systems.
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification.
