论文解读
To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection
说话人识别 | 6.8/10
说话人识别
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Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report
说话人验证 | 5.5/10
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Evaluating voice anonymisation using similarity rank disclosure
Evaluating voice anonymisation using similarity rank disclosure
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TARNet: A Temporal-Aware Multi-Scale Architecture for Closed-Set Speaker Identification
TARNet: A Temporal-Aware Multi-Scale Architecture for Closed-Set Speaker Identification
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Multi-Axis Speech Similarity via Factor-Partitioned Embeddings
音频检索 | 6.0/10
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Spoken Language Identification with Pre-trained Models and Margin Loss
说话人识别 | 7.5/10
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Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe
模型评估 | 7.5/10
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A Personalized Real-Time Proactive Voice Memory Assistant
实时处理 | 7.0/10
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Assessing the Impact of Speaker Identity in Speech Spoofing Detection
音频深度伪造检测 | 8.0/10
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Auden-Voice: General-Purpose Voice Encoder for Speech and Language Understanding
语音编码器 | 7.5/10
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Does the Pre-Training of an Embedding Influence its Encoding of Age?
语音生物标志物 | 7.0/10
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DPO-Regularized Regression for Age Prediction
说话人识别 | 7.5/10
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Erasing Your Voice Before it’s Heard: Training-Free Speaker Unlearning for Zero-Shot Text-to-Speech
语音合成 | 7.5/10
会议任务专题
ICASSP 2026 - 说话人识别
共 1 篇 ICASSP 2026 说话人识别 方向论文
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Improving the Speaker Anonymization Evaluation’s Robustness to Target Speakers with Adversarial Learning
语音匿名化 | 7.5/10
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PRSA: Preventing Malicious Speaker Recognition and Speech Synthesis Simultaneously with Adversarial Examples
语音匿名化 | 7.0/10
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UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition
说话人验证 | 7.5/10
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Explainable AI in Speaker Recognition -- Making Latent Representations Understandable
说话人识别 | 7.5/10
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Where Do Self-Supervised Speech Models Become Unfair?
这篇论文旨在探究自监督语音模型（S3M）的不公平性究竟在模型的哪个层级产生。研究团队采用了一种轻量级的线性探针方法，在多个S3M（如WavLM, Wav2Vec2, BEST-RQ, Whisper）的每一层嵌入上，同时评估了说话人识别（SID）和自动语音识别（ASR）任务的整体性能及对不同说话人组
论文解读
SpeakerRPL v2: Robust Open-set Speaker Identification through Enhanced Few-shot Foundation Tuning and Model Fusion
本文旨在解决开放集说话人识别中的鲁棒性问题，即系统在仅有少量目标说话人注册样本的情况下，需同时准确识别已知说话人并可靠拒识未知说话人。作者在先前SpeakerRPL V1框架基础上提出了三项关键改进：