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SCORE: Scaling audio generation using Standardized COmposite REwards
Jaemin Jung, Jaehun Kim, Inkyu Shin +1
The goal of this paper is to enhance Text-to-Audio generation at inference, focusing on generating realistic audio that precisely aligns with text prompts. Despite the rapid advanc…
SPADE: Structured Pruning and Adaptive Distillation for Efficient LLM-TTS
Tan Dat Nguyen, Jaehun Kim, Ji-Hoon Kim +3
The goal of this paper is to introduce SPADE, a framework for Structured Pruning and Adaptive Distillation for Efficient Large Language Model-based text-to-speech (LLM-TTS). Recent…
From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech
Ji-Hoon Kim, Jeongsoo Choi, Jaehun Kim +2
The objective of this study is to generate high-quality speech from silent talking face videos, a task also known as video-to-speech synthesis. A significant challenge in video-to-…
FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder
Tan Dat Nguyen, Ji-Hoon Kim, Youngjoon Jang +2
The goal of this paper is to generate realistic audio with a lightweight and fast diffusion-based vocoder, named FreGrad. Our framework consists of the following three key componen…
Seeing Through the Conversation: Audio-Visual Speech Separation based on Diffusion Model
Suyeon Lee, Chaeyoung Jung, Youngjoon Jang +2
The objective of this work is to extract target speaker's voice from a mixture of voices using visual cues. Existing works on audio-visual speech separation have demonstrated their…