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Towards Blind Data Cleaning: A Case Study in Music Source Separation
音乐信息检索 | 7.0/10
鲁棒性
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Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments
语音增强 | 8.5/10
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Towards Robust Dysarthric Speech Recognition: LLM-Agent Post-ASR Correction Beyond WER
语音识别 | 9.0/10
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Training Flow Matching Models with Reliable Labels via Self-Purification
语音合成 | 7.5/10
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Transferable Audio Lottery Tickets: Gradient Accumulation for Extreme Sparsity
音频分类 | 7.0/10
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Two-Stage Language Model Framework for Acoustic Echo Cancellation
语音增强 | 7.5/10
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UMV: A Mixture-Of-Experts Vision Transformer with Multi-Spectrogram Fusion for Underwater Ship Noise Classification
音频分类 | 7.5/10
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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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Unseen but Not Unknown: Using Dataset Concealment to Robustly Evaluate Speech Quality Estimation Models
语音质量评估 | 8.3/10
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Voting-Based Pitch Estimation with Temporal and Frequential Alignment and Correlation Aware Selection
语音识别 | 8.0/10
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Wave-Trainer-Fit: Neural Vocoder With Trainable Prior And Fixed-Point Iteration Towards High-Quality Speech Generation From SSL Features
语音合成 | 7.0/10
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When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
模型评估 | 7.0/10
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When Voice Matters: A Controlled Study of Audio LLM Behavior in Clinical Decision-Making
模型评估 | 7.0/10
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RAS: a Reliability Oriented Metric for Automatic Speech Recognition
语音识别 | 7.5/10
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Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss
音频检索 | 7.5/10
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Advancing automatic speech recognition using feature fusion with self-supervised learning features: A case study on Fearless Steps Apollo corpus
语音识别 | 7.0/10
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"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias
语音识别 | 7.0/10
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Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition
语音识别 | 7.5/10
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Before the Mic: Physical-Layer Voiceprint Anonymization with Acoustic Metamaterials
这篇论文针对在公共场景（如会议、演讲）中，不可信录音设备可能导致声纹泄露且事后无法补救的问题，提出了EchoMask——首个基于声学超材料的物理层实时声纹匿名化系统。其核心方法是在声音到达麦克风前，通过精心设计的被动声学结构对特定低频段（300-700Hz）进行选择性干扰，该频段对说话人识别至关重要
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Enhancing Speaker Verification with Whispered Speech via Post-Processing
1. **问题**：耳语语音因缺乏声带振动，其声学特征与正常语音差异显著，导致现有的说话人验证系统性能严重下降。这在用户为保护隐私而低语、或因疾病无法正常发声等实际场景中构成挑战。 2. **方法核心**：在预训练的说话人验证骨干网络（ReDimNet-B6）之上，添加一个轻量级的编码器-解码器结构