论文解读
Domain Partitioning Meets Parameter-Efficient Fine-Tuning: A Novel Method for Improved Language-Queried Audio Source Separation
音频分离 | 7.5/10
预训练
论文解读
Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems
语音对话系统 | 7.0/10
论文解读
Efficient Audio-Visual Inference Via Token Clustering And Modality Fusion
音频问答 | 7.5/10
论文解读
Efficient Depression Detection from Speech via Language-Independent Prompt-Driven Reprogramming
语音生物标志物 | 7.5/10
论文解读
Emotional Dimension Control in Language Model-Based Text-To-Speech: Spanning a Broad Spectrum of Human Emotions
语音合成 | 7.5/10
论文解读
Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation Guided Structured Pruning
说话人验证 | 7.5/10
论文解读
Enhancing Speech Intelligibility Prediction for Hearing Aids with Complementary Speech Foundation Model Representations
语音增强 | 7.5/10
论文解读
Exploring How Audio Effects Alter Emotion with Foundation Models
音乐理解 | 7.0/10
论文解读
FUSEMOS: Perceptual Evaluation of Text-to-Music Generation with Dual-Encoder Fusion and Ranking-Aware Composite Loss
音乐生成 | 7.5/10
论文解读
Gen-SER: When the Generative Model Meets Speech Emotion Recognition
语音情感识别 | 6.5/10
论文解读
GLAP: General Contrastive Audio-Text Pretraining Across Domains and Languages
音频检索 | 8.5/10
论文解读
GLUE: Gradient-free Learning to Unify Experts
迁移学习 | 6.5/10
论文解读
Graph-Biased EEG Transformers for Silent Speech Decoding
语音生物标志物 | 6.5/10
论文解读
Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models
音频检索 音频分类 | 8.0/10
论文解读
Hierarchical Activity Recognition and Captioning from Long-Form Audio
音频事件检测 | 7.5/10
论文解读
High-Fidelity Speech Enhancement Via Discrete Audio Tokens
语音增强 | 7.5/10
论文解读
I-DCCRN-VAE: An Improved Deep Representation Learning Framework for Complex VAE-Based Single-Channel Speech Enhancement
语音增强 | 7.5/10
会议任务专题
ICASSP 2026 - 预训练
共 1 篇 ICASSP 2026 预训练 方向论文
论文解读
Improving Anomalous Sound Detection with Attribute-Aware Representation from Domain-Adaptive Pre-Training
音频事件检测 | 8.0/10
论文解读
Leveraging Large Speech Language Models as Evaluators for Expressive Speech
语音情感识别 | 6.5/10