109 citations · 276 across the 28 of their papers we have counts for
28 papers
Self-supervised Complex Network for Machine Sound Anomaly Detection
Miseul Kim, Minh Tri Ho, Hong-Goo Kang
In this paper, we propose an anomaly detection algorithm for machine sounds with a deep complex network trained by self-supervision. Using the fact that phase continuity informatio…
Style Modeling for Multi-Speaker Articulation-to-Speech
Miseul Kim, Zhenyu Piao, Jihyun Lee +1
In this paper, we propose a neural articulation-to-speech (ATS) framework that synthesizes high-quality speech from articulatory signal in a multi-speaker situation. Most conventio…
Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech
Hyungchan Yoon, Changhwan Kim, Eunwoo Song +2
For personalized speech generation, a neural text-to-speech (TTS) model must be successfully implemented with limited data from a target speaker. To this end, the baseline TTS mode…
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement
Hejung Yang, Hong-Goo Kang
Large, pre-trained representation models trained using self-supervised learning have gained popularity in various fields of machine learning because they are able to extract high-q…
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…
HappyQuokka System for ICASSP 2023 Auditory EEG Challenge
Zhenyu Piao, Miseul Kim, Hyungchan Yoon +1
This report describes our submission to Task 2 of the Auditory EEG Decoding Challenge at ICASSP 2023 Signal Processing Grand Challenge (SPGC). Task 2 is a regression problem that f…
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…
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence
Sangshin Oh, Seyun Um, Hong-Goo Kang
The Gumbel-softmax distribution, or Concrete distribution, is often used to relax the discrete characteristics of a categorical distribution and enable back-propagation through dif…
Adversarial Learning of Intermediate Acoustic Feature for End-to-End Lightweight Text-to-Speech
Hyungchan Yoon, Seyun Um, Changwhan Kim +1
To simplify the generation process, several text-to-speech (TTS) systems implicitly learn intermediate latent representations instead of relying on predefined features (e.g., mel-s…
SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +7
ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or conve…
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement
Doyeon Kim, Hyewon Han, Hyeon-Kyeong Shin +2
Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly. However, these loss terms…
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations
Se-Yun Um, Jihyun Kim, Jihyun Lee +1
In this paper, we propose a multi-speaker face-to-speech waveform generation model that also works for unseen speaker conditions. Using a generative adversarial network (GAN) with…
Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation
Jiyoung Lee, Soo-Whan Chung, Sunok Kim +2
In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional approaches utilize frame-wise matc…
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…
FaceFilter: Audio-visual speech separation using still images
Soo-Whan Chung, Soyeon Choe, Joon Son Chung +1
The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network. Unlike previous works that…
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision
Soo-Whan Chung, Hong Goo Kang, Joon Son Chung
The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data. Recent advances in self-supervised learning have shown that effec…
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
Min-Jae Hwang, Eunwoo Song, Ryuichi Yamamoto +2
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully…
Emotional speech synthesis with rich and granularized control
Se-Yun Um, Sangshin Oh, Kyungguen Byun +3
This paper proposes an effective emotion control method for an end-to-end text-to-speech (TTS) system. To flexibly control the distinct characteristic of a target emotion category,…
Parameter Enhancement for MELP Speech Codec in Noisy Communication Environment
Min-Jae Hwang, Hong-Goo Kang
In this paper, we propose a deep learning (DL)-based parameter enhancement method for a mixed excitation linear prediction (MELP) speech codec in noisy communication environment. U…
Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systems
Ohsung Kwon, Eunwoo Song, Jae-Min Kim +1
In this paper, we propose a high-quality generative text-to-speech (TTS) system using an effective spectrum and excitation estimation method. Our previous research verified the eff…
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…