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Speaker Recognition
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Caiyq2019/DNF
공식 코드https://github.com/Caiyq2019/Deep-generative-LDA
공식 코드https://github.com/Caiyq2019/MG
공식 코드Deep Speaker Vector Normalization with Maximum Gaussianality Training
https://github.com/DanielMengLiu/DeepLip
공식 코드Exploring Deep Learning for Joint Audio-Visual Lip Biometrics
https://github.com/Edresson/Speech2Phone
공식 코드Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models
https://github.com/IDRnD/ReDimNet
공식 코드https://github.com/Jungjee/RawNet
공식 코드https://github.com/KinWaiCheuk/MCE2018
공식 코드https://github.com/MahdiHajibabaei/unified-embedding
공식 코드https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation
공식 코드https://github.com/PaddlePaddle/PaddleSpeech
공식 코드https://github.com/QuwsarOhi/u-vectors
공식 코드U-vectors: Generating clusterable speaker embedding from unlabeled data
https://github.com/SEC4SR/SEC4SR
공식 코드SEC4SR: A Security Analysis Platform for Speaker Recognition
https://github.com/TAMU-VITA/AutoSpeech
공식 코드AutoSpeech: Neural Architecture Search for Speaker Recognition
https://github.com/WeidiXie/VGG-Speaker-Recognition
공식 코드Utterance-level Aggregation For Speaker Recognition In The Wild
https://github.com/a43992899/noisyspeakerdetection
공식 코드Inconsistency Ranking-based Noisy Label Detection for High-quality Data
https://github.com/aditthapron/windowmasking
공식 코드https://github.com/airi-institute/asi-certification
공식 코드Certification of Speaker Recognition Models to Additive Perturbations
https://github.com/alibaba-damo-academy/3D-Speaker
공식 코드3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization
https://github.com/allen-piexl/deepsensor
공식 코드Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
https://github.com/andi611/Self-Supervised-Speech-Pretraining-and-Representation-Learning
공식 코드Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
https://github.com/annie-gu/MVAEBasedBSE
공식 코드https://github.com/ashi-ta/speechglue
공식 코드SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
https://github.com/awslabs/speech-representations
공식 코드https://github.com/bandas-center/atrain
공식 코드Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews
https://github.com/bsxfan/PSDA
공식 코드https://github.com/bsxfan/toroidal-psda
공식 코드https://github.com/carlosfranzreb/private_knnvc
공식 코드Private kNN-VC: Interpretable Anonymization of Converted Speech
https://github.com/chenllliang/CTDNN-Crossed-Time-Delay-Neural-Network-for-Speaker-Recognition
공식 코드https://github.com/chenllliang/ctdnn
공식 코드https://github.com/clovaai/voxceleb_trainer
공식 코드https://github.com/coolEphemeroptera/AESRC2020
공식 코드https://github.com/cvqluu/MTL-Speaker-Embeddings
공식 코드https://github.com/deeplsd/merkel-podcast-corpus
공식 코드https://github.com/domiwk/privacy-preserving-ad-detection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.