https://github.com/astorfi/3D-convolutional-speaker-recognition
공식 코드Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Speaker verification is the verifying the identity of a person from characteristics of the voice.
<span style="color:grey; opacity: 0.6">( Image credit: Contrastive-Predictive-Coding-PyTorch )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection
Learning spectro-temporal representations of complex sounds with parameterized neural networks
Analysis of impact of emotions on target speech extraction and speech separation
SALMONN: Towards Generic Hearing Abilities for Large Language Models
From Speaker Verification to Multispeaker Speech Synthesis, Deep Transfer with Feedback Constraint
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds
DropClass and DropAdapt: Dropping classes for deep speaker representation learning
Region-Based Optimization in Continual Learning for Audio Deepfake Detection
Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification
Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models
NeXt-TDNN: Modernizing Multi-Scale Temporal Convolution Backbone for Speaker Verification
Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection
Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT
FilterAugment: An Acoustic Environmental Data Augmentation Method
Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.