Search Results for author:
Found 6 papers, 1 papers with code
SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement
This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE).
Low-Resource Text-to-Speech Using Specific Data and Noise Augmentation
These architectures must be trained with tens of hours of annotated and high-quality speech data.
NESC: Robust Neural End-2-End Speech Coding with GANs
Neural networks have proven to be a formidable tool to tackle the problem of speech coding at very low bit rates.
PostGAN: A GAN-Based Post-Processor to Enhance the Quality of Coded Speech
In order to mitigate these coding artefacts and enhance the quality of coded speech, a post-processor that relies on a-priori information transmitted from the encoder is traditionally employed at the decoder side.
A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate
Recently, GAN vocoders have seen rapid progress in speech synthesis, starting to outperform autoregressive models in perceptual quality with much higher generation speed.
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech.
