Speaker Recognition
90 papers with code • 1 benchmarks • 6 datasets
Speaker Recognition is the process of identifying or confirming the identity of a person given his speech segments.
Source: Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition
Benchmarks
These leaderboards are used to track progress in Speaker Recognition
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Libraries
Use these libraries to find Speaker Recognition models and implementationsDatasets
VoxCeleb1
VGG-Sound
CN-CELEB
Most implemented papers
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
PaddlePaddle/PaddleSpeech
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NAACL (ACL) 2022
PaddleSpeech is an open-source all-in-one speech toolkit.
Toroidal Probabilistic Spherical Discriminant Analysis
It extends PSDA with the ability to model within and between-speaker variabilities in toroidal submanifolds of the hypersphere.
TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement
We propose an objective for perceptual quality based on temporal acoustic parameters.
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models
espnet/espnet
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First, we provide an open-source platform for researchers in the speaker recognition community to effortlessly build models.
Unified Hypersphere Embedding for Speaker Recognition
MahdiHajibabaei/unified-embedding
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Incremental improvements in accuracy of Convolutional Neural Networks are usually achieved through use of deeper and more complex models trained on larger datasets.
Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model
In this paper, we propose a Convolutional Neural Network (CNN) based speaker recognition model for extracting robust speaker embeddings.
Additive Margin SincNet for Speaker Recognition
The Softmax loss function is a widely used function in deep learning methods, but it is not the best choice for all kind of problems.
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language Recognition
awslabs/speech-representations
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We introduce BERTphone, a Transformer encoder trained on large speech corpora that outputs phonetically-aware contextual representation vectors that can be used for both speaker and language recognition.
Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks
When reducing the training data to only using the train set, our method results in 309 confusions for the Multi-target speaker identification task, which is 46% better than the baseline model.
Delving into VoxCeleb: environment invariant speaker recognition
theolepage/sslsv
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Research in speaker recognition has recently seen significant progress due to the application of neural network models and the availability of new large-scale datasets.
