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Proficiency-Aware Adaptation and Data Augmentation for Robust L2 ASR
语音识别 | 6.5/10
领域适应
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- 测试时自适应 直接 93 篇 · 含子树 93 篇
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Ranking The Impact of Contextual Specialization in Neural Speech Enhancement
语音增强 | 7.5/10
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SLM-TTA: A Framework for Test-Time Adaptation of Generative Spoken Language Models
语音识别 | 7.0/10
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SONAR: Self-Distilled Continual Pre-Training for Domain Adaptive Audio Representation
音频事件检测 | 7.0/10
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SSVD-O: Parameter-Efficient Fine-Tuning with Structured SVD for Speech Recognition
语音识别 | 7.0/10
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Structure-Aware Diffusion Schrödinger Bridge
数据集对齐 | 7.7/10
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Synthetic Data Domain Adaptation for ASR via LLM-Based Text and Phonetic Respelling Augmentation
语音识别 | 8.0/10
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Teaching the Teachers: Boosting Unsupervised Domain Adaptation In Speech Recognition By Ensemble Update
语音识别 | 7.0/10
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Test Time Adaptation for Speech Emotion Recognition
语音情感识别 | 7.0/10
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The Impact of Audio Watermarking on Audio Anti-Spoofing Countermeasures
音频深度伪造检测 | 8.5/10
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The Synergistic Role of Audio and Large Video-Language Model in Source-Free Video Domain Adaptation
领域适应 | 7.0/10
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Towards Fair ASR for Second Language Speakers using Fairness Prompted Finetuning
语音识别 | 6.5/10
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Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition
语音识别 | 7.5/10
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Vioptt: Violin Technique-Aware Transcription from Synthetic Data Augmentation
音乐信息检索 | 6.5/10
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When Children Talk and Machines Listen: Toward an Interpretable Speech-Based Screener for Dutch Developmental Language Disorder
语音生物标志物 | 7.0/10
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Whisper: Courtside Edition - Enhancing ASR Performance through LLM-Driven Context Generation
语音识别 | 6.5/10
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Prosody as Supervision: Bridging the Non-Verbal--Verbal for Multilingual Speech Emotion Recognition
语音情感识别 | 8.0/10
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Enhancing ASR Performance in the Medical Domain for Dravidian Languages
这篇论文旨在解决达罗毗荼语言（Telugu和Kannada）在医疗领域自动语音识别（ASR）中面临的标注数据稀缺和语言形态复杂两大挑战。其核心方法是提出一个“置信度感知训练框架”，该框架通过一个混合置信度评分机制（结合静态的感知、声学相似性、WER分数和动态的模型熵），对混合了真实与合成语音的训练数
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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
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Tadabur: A Large-Scale Quran Audio Dataset
1. **问题**：现有的古兰经语音数据集在规模、诵读者多样性、音频质量和标注深度上存在严重不足，限制了古兰经ASR、诵读者识别等任务的研究进展。 2. **方法核心**：提出Tadabur数据集及其构建流水线。流水线核心是“古兰经经文对齐模块”（AAM），它结合WhisperX进行初步转录，再利用