activity
20242026
most citedMMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark

1 citations · 2 across the 11 of their papers we have counts for

collaborators

13 papers

cs.SD2026

EchoChat: Structured Cognitive Reasoning in Empathetic Spoken Dialogue

Dingdong Wang, Shujie Liu, Yayue Deng +6

Empathetic spoken dialogue is a sophisticated cognitive process that requires not only recognizing emotions but also inferring a user's latent mental states to provide appropriate…

cs.SD2026

DuraS2ST: Chain-of-Thought and Reinforcement Learning for Duration-Aligned Speech-to-Speech Translation

Yayue Deng, Dingdong Wang, Yuxuan Hu +7

Speech-to-speech translation (S2ST) in time-sensitive applications such as video dubbing requires not only semantic fidelity and speaker preservation, but also strict duration cons…

cs.SD2026

SonicWeave: Chunk-Routed Mixture-of-Experts for Unified Audio Scene Generation

Yunrui Cai, Xu Li, Yucheng Zhou +8

Text-conditioned general audio generation is moving beyond isolated speech, music, and sound-effect synthesis toward a single model that can compose them into controllable, coheren…

cs.CL2026

TriggerBench: Investigating Prospective Memory for Large Language Models

Tianhua Zhang, Xinjiang Wang, Qianxi Zhang +6

While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prosp…

cs.AI2026

AVBench: Human-Aligned and Automated Evaluation Benchmark for Audio-Video Generative Models

Jialiang Yang, Bin Xia, Ruihang Chu +6

Rapid advances in audio-video (AV) generation have enabled high-fidelity synthesis with synchronized sound, particularly for human-related scenarios involving speech and interactio…

cs.SD2026

V2A-DPO: Omni-Preference Optimization for Video-to-Audio Generation

Nolan Chan, Timmy Gang, Yongqian Wang +2

This paper introduces V2A-DPO, a novel Direct Preference Optimization (DPO) framework tailored for flow-based video-to-audio generation (V2A) models, incorporating key adaptations…

cs.SD2026

EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning

Dingdong Wang, Shujie Liu, Tianhua Zhang +3

Emotional information in speech plays a unique role in multimodal perception. However, current Speech Large Language Models (SpeechLLMs), similar to conventional speech emotion rec…

cs.CL2025

Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs

Dingdong Wang, Junan Li, Mingyu Cui +3

With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has d…

cs.CL2025★ 1 cited

MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark

Dingdong Wang, Junan Li, Jincenzi Wu +4

Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken language understanding, effective interpretation often requi…

cs.SD2025

InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training

Dingdong Wang, Jin Xu, Ruihang Chu +6

Recent advancements in speech large language models (SpeechLLMs) have attracted considerable attention. Nonetheless, current methods exhibit suboptimal performance in adhering to s…

cs.CL2024

A Comparative Study of Discrete Speech Tokens for Semantic-Related Tasks with Large Language Models

Dingdong Wang, Mingyu Cui, Dongchao Yang +2

With the rise of Speech Large Language Models (Speech LLMs), there has been growing interest in discrete speech tokens for their ability to integrate with text-based tokens seamles…

cs.SD2024★ 1 cited

CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction

Xueyuan Chen, Dongchao Yang, Dingdong Wang +3

Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech. It still suffers from low speaker similarity and poor prosody naturalness. In this pa…

cs.SD2024

SimpleSpeech: Towards Simple and Efficient Text-to-Speech with Scalar Latent Transformer Diffusion Models

Dongchao Yang, Dingdong Wang, Haohan Guo +3

In this study, we propose a simple and efficient Non-Autoregressive (NAR) text-to-speech (TTS) system based on diffusion, named SimpleSpeech. Its simpleness shows in three aspects:…