12 citations · 24 across the 8 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
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…
Qwen3-Omni Technical Report
Jin Xu, Zhifang Guo, Hangrui Hu +35
We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relat…
Qwen2.5-Omni Technical Report
Jin Xu, Zhifang Guo, Jinzheng He +11
In this report, we present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously gene…
FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs
Keyu An, Qian Chen, Chong Deng +30
This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs). At its core are two innovative mo…