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Found 6 papers, 1 papers with code
Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Range
This paper proposes a scalable distributed policy gradient method and proves its convergence to near-optimal solution in multi-agent linear quadratic networked systems.
Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech
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.
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
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
Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement
As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing.
AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style
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.
AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data
In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.
