6 papers · 1 filter
SphereVAE: Hyperspherical Latent Autoencoders for Robust Autoregressive Speech Representation Modeling
Haoyu Zhang, Jingbin Hu, Hanke Xie +8
With the rapid development of speech generation technology, discrete codec representations have been widely used because they provide a stable prediction paradigm. In expressive sp…
SemBridge: Semantic Token Anchoring for Continuous-Latent Autoregressive Speech Generation
Hanke Xie, Haopeng Lin, Jiale Qian +13
Continuous-latent autoregressive speech generation has emerged as a promising alternative to discrete-token modeling by avoiding quantization loss and preserving richer acoustic in…
FlashTTS: Fast Streaming TTS with MTP Acceleration and X-pred Mean Flow Distillation
Hanke Xie, Xiaming Ren, Dake Guo +10
Recent progress in speech dialogue systems requires Text-to-Speech (TTS) models to be faster and more responsive. Modern speech dialogue systems impose two primary requirements on…
MINT-Bench: A Comprehensive Multilingual Benchmark for Instruction-Following Text-to-Speech
Huakang Chen, Jingbin Hu, Liumeng Xue +12
Instruction-following text-to-speech (TTS) has emerged as an important capability for controllable and expressive speech generation, yet its evaluation remains underdeveloped due t…
VoiceSculptor: Your Voice, Designed By You
Jingbin Hu, Huakang Chen, Linhan Ma +19
Despite rapid progress in text-to-speech (TTS), open-source systems still lack truly instruction-following, fine-grained control over core speech attributes (e.g., pitch, speaking…
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…