3 papers
cs.LG2026
Semantic Tube Prediction: Beating LLM Data Efficiency with JEPA
Hai Huang, Yann LeCun, Randall Balestriero
Large Language Models (LLMs) obey consistent scaling laws -- empirical power-law fits that predict how loss decreases with compute, data, and parameters. While predictive, these la…
eess.AS2025
ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control
Shengpeng Ji, Qian Chen, Wen Wang +8
In this paper, we present ControlSpeech, a text-to-speech (TTS) system capable of fully cloning the speaker's voice and enabling arbitrary control and adjustment of speaking style.…
eess.AS2025
Language-Codec: Bridging Discrete Codec Representations and Speech Language Models
Shengpeng Ji, Minghui Fang, Jialong Zuo +5
In recent years, large language models have achieved significant success in generative tasks related to speech, audio, music, and other signal domains. A crucial element of these m…