109 citations · 258 across the 25 of their papers we have counts for
14 papers · 1 filter
Seeing What You Say: Expressive Image Generation from Speech
Jiyoung Lee, Song Park, Sanghyuk Chun +1
This paper proposes VoxStudio, the first unified and end-to-end speech-to-image model that generates expressive images directly from spoken descriptions by jointly aligning linguis…
Listen through the Sound: Generative Speech Restoration Leveraging Acoustic Context Representation
Soo-Whan Chung, Min-Seok Choi
This paper introduces a novel approach to speech restoration by integrating a context-related conditioning strategy. Specifically, we employ the diffusion-based generative restorat…
Speak in the Scene: Diffusion-based Acoustic Scene Transfer toward Immersive Speech Generation
Miseul Kim, Soo-Whan Chung, Youna Ji +2
This paper introduces a novel task in generative speech processing, Acoustic Scene Transfer (AST), which aims to transfer acoustic scenes of speech signals to diverse environments.…
HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders
Doyeon Kim, Soo-Whan Chung, Hyewon Han +2
This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments. Unlike conventional approaches that…
MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion
Woo-Jin Chung, Doyeon Kim, Soo-Whan Chung +1
We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in…
An empirical study on speech restoration guided by self supervised speech representation
Jaeuk Byun, Youna Ji, Soo Whan Chung +2
Enhancing speech quality is an indispensable yet difficult task as it is often complicated by a range of degradation factors. In addition to additive noise, reverberation, clipping…
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition
Yoohwan Kwon, Soo-Whan Chung
Multi-lingual speech recognition aims to distinguish linguistic expressions in different languages and integrate acoustic processing simultaneously. In contrast, current multi-ling…
Diffusion-based Generative Speech Source Separation
Robin Scheibler, Youna Ji, Soo-Whan Chung +3
We propose DiffSep, a new single channel source separation method based on score-matching of a stochastic differential equation (SDE). We craft a tailored continuous time diffusion…
Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting
Hyeon-Kyeong Shin, Hyewon Han, Doyeon Kim +2
In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences. Unlike prev…
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +6
The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scen…
Learning in your voice: Non-parallel voice conversion based on speaker consistency loss
Yoohwan Kwon, Soo-Whan Chung, Hee-Soo Heo +1
In this paper, we propose a novel voice conversion strategy to resolve the mismatch between the training and conversion scenarios when parallel speech corpus is unavailable for tra…
MIRNet: Learning multiple identities representations in overlapped speech
Hyewon Han, Soo-Whan Chung, Hong-Goo Kang
Many approaches can derive information about a single speaker's identity from the speech by learning to recognize consistent characteristics of acoustic parameters. However, it is…
Intra-class variation reduction of speaker representation in disentanglement framework
Yoohwan Kwon, Soo-Whan Chung, Hong-Goo Kang
In this paper, we propose an effective training strategy to ex-tract robust speaker representations from a speech signal. Oneof the key challenges in speaker recognition tasks is t…
Orthonormal Embedding-based Deep Clustering for Single-channel Speech Separation
Soyeon Choe, Soo-Whan Chung, Youna Ji +1
Deep clustering is a deep neural network-based speech separation algorithm that first trains the mixed component of signals with high-dimensional embeddings, and then uses a cluste…