19 citations · 19 across the 8 of their papers we have counts for
5 papers · 1 filter
VoiceDesigner: Text-to-Voice Generation and Editing via Unified Diffusion Modeling and Data Augmentation
Jiarui Hai, Karan Thakkar, Ke Chen +5
Recent breakthroughs in generative models have made text-to-voice generation (TTV) possible, enabling the synthesis of speech directly from textual voice descriptions. However, exi…
SpeechOp: Inference-Time Task Composition for Generative Speech Processing
Justin Lovelace, Rithesh Kumar, Jiaqi Su +3
While generative Text-to-Speech (TTS) systems leverage vast ``in-the-wild" data to achieve remarkable success, speech-to-speech processing tasks like enhancement face data limitati…
DiTSE: High-Fidelity Generative Speech Enhancement via Latent Diffusion Transformers
Heitor R. Guimarães, Jiaqi Su, Rithesh Kumar +2
Real-world speech recordings suffer from degradations such as background noise and reverberation. Speech enhancement aims to mitigate these issues by generating clean high-fidelity…
Code Drift: Towards Idempotent Neural Audio Codecs
Patrick O'Reilly, Prem Seetharaman, Jiaqi Su +2
Neural codecs have demonstrated strong performance in high-fidelity compression of audio signals at low bitrates. The token-based representations produced by these codecs have prov…
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks
Jiaqi Su, Zeyu Jin, Adam Finkelstein
Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to trans…