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20242026
most citedLarge Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition

10 citations · 13 across the 20 of their papers we have counts for

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6 papers · 1 filter

cs.SD2026

Long Listening Thoughts: Eliciting Open Auditory Reasoning with Deliberative Perception and Cognitive Refinement

Jaeyeon Kim, Chao-Han Huck Yang, Luoyi Zhang +5

We introduce Long Listening Thoughts (LLT), a framework for constructing post-training datasets that improves audio reasoning by pairing deliberate perceptual grounding with explic…

cs.SD2026

CrossEdit: Cross-Modal Training Enables Rich Audio-Visual Editing

William Chen, Prem Seetharaman, Ke Chen +10

Multimedia editing requires generative models to interpret complex, compositional instructions and modify only the desired elements across modalities, a capability largely beyond e…

cs.SD2026

Online Predictive Coding for Dual-Mode Self-Supervised Speech Model

Keita Goto, Takashi Maekaku, Jin Sakuma +3

Dual-mode self-supervised speech models are pre-trained to handle streaming and non-streaming conditions simultaneously. However, their attention is computed over different context…

cs.SD2026

Bagpiper-Edit: Zero-Shot Open-Ended Audio Editing via Rich-Caption

Xun Gong, Jinchuan Tian, Haoran Wang +3

Current text-guided audio editing methods rely on paired training data, predefined operation templates, and separate processing pipelines across speech, music, and sound. We presen…

cs.SD2026

Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception

Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian +15

We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is…

cs.SD2025

UALM: Unified Audio Language Model for Understanding, Generation and Reasoning

Jinchuan Tian, Sang-gil Lee, Zhifeng Kong +11

Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. Very few studies attempt to unify these tasks…