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Found 16 papers, 3 papers with code
Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
To this end, we propose to generalize the standalone ASV (G-SASV) against spoofing attacks, where we leverage limited training data from CM to enhance a simple backend in the embedding space, without the involvement of a separate CM module during the test (authentication) phase.
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.
Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification
Even though deep speaker models have demonstrated impressive accuracy in speaker verification tasks, this often comes at the expense of increased model size and computation time, presenting challenges for deployment in resource-constrained environments.
I4U System Description for NIST SRE'20 CTS Challenge
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
Baselines and Protocols for Household Speaker Recognition
Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests).
Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation
In this paper, we initiate the concern of enhancing the spoofing robustness of the automatic speaker verification (ASV) system, without the primary presence of a separate countermeasure module.
Learnable Nonlinear Compression for Robust Speaker Verification
We consider different kinds of channel-dependent (CD) nonlinear compression methods optimized in a data-driven manner.
Optimizing Multi-Taper Features for Deep Speaker Verification
Multi-taper estimators provide low-variance power spectrum estimates that can be used in place of the windowed discrete Fourier transform (DFT) to extract speech features such as mel-frequency cepstral coefficients (MFCCs).
Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification
After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification.
Parameterized Channel Normalization for Far-field Deep Speaker Verification
We address far-field speaker verification with deep neural network (DNN) based speaker embedding extractor, where mismatch between enrollment and test data often comes from convolutive effects (e. g. room reverberation) and noise.
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.
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.
Learnable MFCCs for Speaker Verification
We propose a learnable mel-frequency cepstral coefficient (MFCC) frontend architecture for deep neural network (DNN) based automatic speaker verification.
A Comparative Re-Assessment of Feature Extractors for Deep Speaker Embeddings
Modern automatic speaker verification relies largely on deep neural networks (DNNs) trained on mel-frequency cepstral coefficient (MFCC) features.
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.
AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale
For research community, we hope that AISHELL-2 corpus can be a solid resource for topics like transfer learning and robust ASR.
