1 citations · 2 across the 27 of their papers we have counts for
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LP-CFM: Perceptual Invariance-Aware Conditional Flow Matching for Speech Modeling
Doyeop Kwak, Youngjoon Jang, Joon Son Chung
The goal of this paper is to provide a new perspective on speech modeling by incorporating perceptual invariances such as amplitude scaling and temporal shifts. Conventional genera…
InfiniteAudio: Infinite-Length Audio Generation with Consistency
Chaeyoung Jung, Hojoon Ki, Ji-Hoon Kim +2
This paper presents InfiniteAudio, a simple yet effective strategy for generating infinite-length audio using diffusion-based text-to-audio methods. Current approaches face memory…
EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
Doyeop Kwak, Youngjoon Jang, Seongyu Kim +1
Speech signals in real-world environments are frequently affected by various distortions such as additive noise, reverberation, and bandwidth limitation, which may appear individua…
Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment
Jeongsoo Choi, Zhikang Niu, Ji-Hoon Kim +3
The goal of this paper is to optimize the training process of diffusion-based text-to-speech models. While recent studies have achieved remarkable advancements, their training dema…
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-…
CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generation
Ji-Hoon Kim, Hong-Sun Yang, Yoon-Cheol Ju +3
The goal of this work is to generate natural speech in multiple languages while maintaining the same speaker identity, a task known as cross-lingual speech synthesis. A key challen…
VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis
Jaemin Jung, Junseok Ahn, Chaeyoung Jung +3
We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for in…
Text-To-Speech Synthesis In The Wild
Jee-weon Jung, Wangyou Zhang, Soumi Maiti +11
Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to util…
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting
Youkyum Kim, Jaemin Jung, Jihwan Park +2
This paper proposes a novel user-defined keyword spotting framework that accurately detects audio keywords based on text enrollment. Since audio data possesses additional acoustic…
VoxSim: A perceptual voice similarity dataset
Junseok Ahn, Youkyum Kim, Yeunju Choi +4
This paper introduces VoxSim, a dataset of perceptual voice similarity ratings. Recent efforts to automate the assessment of speech synthesis technologies have primarily focused on…
FlowAVSE: Efficient Audio-Visual Speech Enhancement with Conditional Flow Matching
Chaeyoung Jung, Suyeon Lee, Ji-Hoon Kim +1
This work proposes an efficient method to enhance the quality of corrupted speech signals by leveraging both acoustic and visual cues. While existing diffusion-based approaches hav…
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…
VoiceLDM: Text-to-Speech with Environmental Context
Yeonghyeon Lee, Inmo Yeon, Juhan Nam +1
This paper presents VoiceLDM, a model designed to produce audio that accurately follows two distinct natural language text prompts: the description prompt and the content prompt. T…