1 citations · 1 across the 11 of their papers we have counts for
11 papers
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…
Qwen-Music Technical Report
Jin Xu, Kangdi Wang, Ruibin Yuan +24
We introduce Qwen-Music, a music generation model that produces high-fidelity songs with complete vocals. It supports text-to-music generation from descriptions, lyrics, and musica…
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…
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…
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…
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…
CosyAudio: Improving Audio Generation with Confidence Scores and Synthetic Captions
Xinfa Zhu, Wenjie Tian, Xinsheng Wang +4
Text-to-Audio (TTA) generation is an emerging area within AI-generated content (AIGC), where audio is created from natural language descriptions. Despite growing interest, developi…
ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training
Xinfa Zhu, Lei He, Yujia Xiao +4
Style voice conversion aims to transform the speaking style of source speech into a desired style while keeping the original speaker's identity. However, previous style voice conve…
KALL-E:Autoregressive Speech Synthesis with Next-Distribution Prediction
Kangxiang Xia, Xinfa Zhu, Jixun Yao +3
We introduce KALL-E, a novel autoregressive (AR) language model for text-to-speech (TTS) synthesis that operates by predicting the next distribution of continuous speech frames. Un…