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20212026
most citedLauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT

18 citations · 101 across the 75 of their papers we have counts for

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10 papers · 1 filter

cs.SD2026

Audio-Oscar: A Multi-Agent System for Complex Audio Scene Generation, Orchestration, and Refinement

Yifan Duan, Qixiang Xu, Hengtao Wu +6

In recent years, audio generation has made significant progress in tasks such as text-to-speech (TTS), text-to-audio (TTA) and text-to-music (TTM). However, generating long-form an…

cs.SD2026

MMAE: A Massive Multitask Audio Editing Benchmark

Ziyang Ma, Ruiqi Yan, Ruiyang Xu +35

We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing.…

eess.AS2026

WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling

Wenxi Chen, Dongya Jia, Yushen Chen +11

Recently, diffusion models operating on VAE latents or mel-spectrograms have become the dominant paradigm for zero-shot TTS. Although these compressed representations improve gener…

eess.AS2026

VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track

Wenming Tu, Jian Gao, Yanru Huo +9

Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or capt…

cs.CL2026

Proactive for Uncertainty: Cause-Aware Error Diagnosis and Interactive Clarification for Spoken Dialogue Systems

Yizhou Peng, Ziyang Ma, Changsong Liu +3

Cascaded Automatic Speech Recognition - Large Language Model (ASR-LLM) pipelines remain popular for industrial Spoken Dialogue Systems (SDS), primarily because their decoupled desi…

eess.AS2026

Evaluating the Expressive Appropriateness of Speech in Rich Contexts

Tianrui Wang, Ziyang Ma, Yizhou Peng +26

Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its con…

eess.AS2026

Anonymization, Not Elimination: Utility-Preserved Speech Anonymization

Yunchong Xiao, Yuxiang Zhao, Ziyang Ma +4

The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degrade data utility, for example b…

cs.SD2026

FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining

Xiquan Li, Xuenan Xu, Ziyang Ma +4

Contrastively pretrained audio-language models (e.g., CLAP) excel at clip-level understanding but struggle with frame-level tasks. Existing extensions fail to exploit the varying g…

cs.SD2026

Audio ControlNet for Fine-Grained Audio Generation and Editing

Haina Zhu, Yao Xiao, Xiquan Li +5

We study the fine-grained text-to-audio (T2A) generation task. While recent models can synthesize high-quality audio from text descriptions, they often lack precise control over at…

cs.SD2026★ 3 cited

SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing

Ziyang Ma, Guanrou Yang, Wenxi Chen +19

The recent surge in open-source Multimodal Large Language Models (MLLM) frameworks, such as LLaVA, provides a convenient kickoff for artificial intelligence developers and research…