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Found 13 papers, 1 papers with code
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).
Extrapolating false alarm rates in automatic speaker verification
Automatic speaker verification (ASV) vendors and corpus providers would both benefit from tools to reliably extrapolate performance metrics for large speaker populations without collecting new speakers.
UIAI System for Short-Duration Speaker Verification Challenge 2020
Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0. 072 and an equal error rate (EER) of 2. 14% on the evaluation set.
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.
Neural i-vectors
To combine the benefits of high performance and generative interpretation, we investigate the use of deep embedding extractor and i-vector extractor in succession.
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.
Voice Biometrics Security: Extrapolating False Alarm Rate via Hierarchical Bayesian Modeling of Speaker Verification Scores
We put forward a novel performance assessment framework to address both the inadequacy of the random-impostor evaluation model and the size limitation of evaluation corpora by addressing ASV security against closest impostors on arbitrarily large datasets.
Unleashing the Unused Potential of I-Vectors Enabled by GPU Acceleration
In particular, we achieve an acceleration of 3000 times in frame posterior computation compared to real time and 25 times in training the i-vector extractor compared to the CPU baseline from Kaldi toolkit.
Voice Mimicry Attacks Assisted by Automatic Speaker Verification
Our goal is to gain insights how well similarity rankings transfer from the attacker's ASV system to the attacked ASV system, whether the attackers are able to improve their attacks by mimicking, and how the properties of the voices of attackers change due to mimicking.
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).
Can We Use Speaker Recognition Technology to Attack Itself? Enhancing Mimicry Attacks Using Automatic Target Speaker Selection
We consider technology-assisted mimicry attacks in the context of automatic speaker verification (ASV).
Who Do I Sound Like? Showcasing Speaker Recognition Technology by YouTube Voice Search
The popularization of science can often be disregarded by scientists as it may be challenging to put highly sophisticated research into words that general public can understand.
Audio and Speech Processing Sound
Supervector Compression Strategies to Speed up I-Vector System Development
The results suggest that, in terms of ASV accuracy, the supervector compression approaches are on a par with FEFA.
