Search Results for author:
Found 6 papers, 3 papers with code
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
We present a neural analysis and synthesis (NANSY) framework that can manipulate voice, pitch, and speed of an arbitrary speech signal.
Real-time Denoising and Dereverberation with Tiny Recurrent U-Net
Modern deep learning-based models have seen outstanding performance improvement with speech enhancement tasks.
From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech
We analyze the extent to which the network can naturally disentangle two latent factors that contribute to the generation of a face image - one that comes directly from a speech signal and the other that is not related to it - and explore whether the network can learn to generate natural human face image distribution by modeling these factors.
Disentangling Timbre and Singing Style with Multi-singer Singing Synthesis System
In this study, we define the identity of the singer with two independent concepts - timbre and singing style - and propose a multi-singer singing synthesis system that can model them separately.
Sound Audio and Speech Processing
Adversarially Trained End-to-end Korean Singing Voice Synthesis System
In this paper, we propose an end-to-end Korean singing voice synthesis system from lyrics and a symbolic melody using the following three novel approaches: 1) phonetic enhancement masking, 2) local conditioning of text and pitch to the super-resolution network, and 3) conditional adversarial training.
Sound Audio and Speech Processing
Phase-aware Speech Enhancement with Deep Complex U-Net
Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.
