Speaker Verification
190 papers with code • 11 benchmarks • 12 datasets
Speaker verification is the verifying the identity of a person from characteristics of the voice.
( Image credit: Contrastive-Predictive-Coding-PyTorch )
Benchmarks
These leaderboards are used to track progress in Speaker Verification
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Libraries
Use these libraries to find Speaker Verification models and implementationsDatasets
VoxCeleb1
VoxCeleb2
VGG-Sound
CN-CELEB
VibraVox (headset microphone)
VibraVox (rigid in-ear microphone)
VibraVox (soft in-ear microphone)
VibraVox (throat microphone)
VibraVox (forehead accelerometer)
Subtasks
Most implemented papers
Generalized End-to-End Loss for Speaker Verification
In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function.
Speaker Recognition from Raw Waveform with SincNet
Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
CorentinJ/Real-Time-Voice-Cloning
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NeurIPS 2018
Clone a voice in 5 seconds to generate arbitrary speech in real-time
ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
The successful x-vector architecture is a Time Delay Neural Network (TDNN) that applies statistics pooling to project variable-length utterances into fixed-length speaker characterizing embeddings.
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
s3prl/s3prl
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To explore this issue, we proposed to employ Mockingjay, a self-supervised learning based model, to protect anti-spoofing models against adversarial attacks in the black-box scenario.
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
astorfi/3D-convolutional-speaker-recognition
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In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speakers' utterances and creation of the speaker model.
Speaker Diarization with LSTM
For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications.
An Unsupervised Autoregressive Model for Speech Representation Learning
iamyuanchung/Autoregressive-Predictive-Coding
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This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations.
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
lixucuhk/ASV-anti-spoofing-with-Res2Net
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ASVspoof, now in its third edition, is a series of community-led challenges which promote the development of countermeasures to protect automatic speaker verification (ASV) from the threat of spoofing.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
Jungjee/RawNet
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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.
