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Found 6 papers, 2 papers with code
A Variance Reduction Method for Neural-based Divergence Estimation
Unfortunately, the approximation of expectations that are inherent in variational formulas by statistical averages can be problematic due to high statistical variance, e. g., exponential for the Kullback-Leibler divergence and certain estimators.
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion
Intelligibility enhancement as quantified by the Intelligibility in Bits (SIIB-Gauss) measure shows that the proposed Lombard-SSDRC TTS system shows significant relative improvement between 110% and 130% in speech-shaped noise (SSN), and 47% to 140% in competing-speaker noise (CSN) against the state-of-the-art TTS approach.
Speaker Conditional WaveRNN: Towards Universal Neural Vocoder for Unseen Speaker and Recording Conditions
In terms of performance, our system has been preferred over the baseline TTS system by 60% over 15. 5% and by 60. 9% over 32. 6%, for seen and unseen speakers, respectively.
Ranked #11 on
Speech Synthesis
on LibriTTS
Cumulant GAN
In this paper, we propose a novel loss function for training Generative Adversarial Networks (GANs) aiming towards deeper theoretical understanding as well as improved stability and performance for the underlying optimization problem.
Training Generative Adversarial Networks with Weights
The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the difficulties in their training.
Robustness of Voice Conversion Techniques Under Mismatched Conditions
Most of the existing studies on voice conversion (VC) are conducted in acoustically matched conditions between source and target signal.
