activity
20242026
most citedQwen3-TTS Technical Report

1 citations · 1 across the 8 of their papers we have counts for

collaborators

9 papers

cs.AI2026

OmniJudge or OmniBias? Diagnosing Multimodal Judges through Balanced, Decoupled Lenses

Guangzheng Hu, Ziyue Jiang, Weixu Qiao +14

Multimodal understanding models that can jointly judge text-to-image (T2I), text-to-video (T2V) and text-to-speech (TTS) generation are increasingly used as "OmniJudges" for evalua…

cs.SD2026

Qwen-Music Technical Report

Jin Xu, Kangdi Wang, Ruibin Yuan +24

In this report, we introduce Qwen-Music, a powerful music generation model capable of producing highly musical and high-fidelity songs with complete vocal singing. Qwen-Music suppo…

cs.SD20261 cited

Qwen3-TTS Technical Report

Hangrui Hu, Xinfa Zhu, Ting He +13

In this report, we present the Qwen3-TTS series, a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Qwen3-TTS supports state-of-the-art 3…

cs.SD2025

PodEval: A Multimodal Evaluation Framework for Podcast Audio Generation

Yujia Xiao, Liumeng Xue, Lei He +8

Recently, an increasing number of multimodal (text and audio) benchmarks have emerged, primarily focusing on evaluating models' understanding capability. However, exploration into…

cs.SD2025

Llasa+: Free Lunch for Accelerated and Streaming Llama-Based Speech Synthesis

Wenjie Tian, Xinfa Zhu, Hanke Xie +3

Recent progress in text-to-speech (TTS) has achieved impressive naturalness and flexibility, especially with the development of large language model (LLM)-based approaches. However…

eess.AS2025

Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis

Zhen Ye, Xinfa Zhu, Chi-Min Chan +17

Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and i…