no code implementations • 5 Mar 2024 • Yuzi Yan, Yuan Shen
This paper proposes a scalable distributed policy gradient method and proves its convergence to near-optimal solution in multi-agent linear quadratic networked systems.
no code implementations • 31 Mar 2022 • Guangyan Zhang, Kaitao Song, Xu Tan, Daxin Tan, Yuzi Yan, Yanqing Liu, Gang Wang, Wei Zhou, Tao Qin, Tan Lee, Sheng Zhao
However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input.
no code implementations • 14 Nov 2021 • Yuzi Yan, Xiaoxiang Li, Xinyou Qiu, Jiantao Qiu, Jian Wang, Yu Wang, Yuan Shen
In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL).
Model Predictive Control Multi-agent Reinforcement Learning +2
no code implementations • 27 Aug 2021 • Yuzi Yan, Wei-Qiang Zhang, Michael T. Johnson
As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing.
no code implementations • 6 Jul 2021 • Yuzi Yan, Xu Tan, Bohan Li, Guangyan Zhang, Tao Qin, Sheng Zhao, Yuan Shen, Wei-Qiang Zhang, Tie-Yan Liu
While recent text to speech (TTS) models perform very well in synthesizing reading-style (e. g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e. g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech.
1 code implementation • 20 Apr 2021 • Yuzi Yan, Xu Tan, Bohan Li, Tao Qin, Sheng Zhao, Yuan Shen, Tie-Yan Liu
In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.