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20242026
most citedQwen2.5-Omni Technical Report

12 citations · 24 across the 8 of their papers we have counts for

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6 papers · 1 filter

eess.AS2026

From Script to Drama: An Agentic Framework for Controllable Multi-Speaker Dialogue TTS

Kangxiang Xia, Xinfa Zhu, HangRui Hu +8

Multi-speaker dialogue TTS requires natural speech generation, consistent speaker identity, coherent cross-turn transitions, and fine-grained control of expressive attributes such…

eess.AS2026

Interactive TTS: Dynamic Speaking Style Adaptation for Expressive Speech Synthesis

Wenjie Tian, Kangxiang Xia, Jingbin Hu +7

Dynamic speaking style adaptation in multi-turn multimodal interaction remains a major challenge for text-to-speech (TTS) systems. Existing context-aware TTS (CTTS) methods typical…

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…

eess.AS2026

DiaScriber: A Speech LLM for Joint Diarization and Transcription in Multi-Speaker Scenarios

Bingshen Mu, Xian Shi, Xiong Wang +6

Multi-speaker automatic speech recognition (MSASR) aims to jointly predict content transcriptions, speaker identities, and timestamps, thereby addressing the key question of "who s…

eess.AS2026

Qwen-Audio-VAE Technical Report

Ziyue Jiang, Dake Guo, Zekai Zhang +11

We introduce \textbf{Qwen-Audio-VAE}, a suite of low-bitrate, fast-encoding continuous audio autoencoders designed for scalable general audio generation. The model is built around…

cs.SD2026★ 1 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…