2 citations · 7 across the 14 of their papers we have counts for
14 papers
SpeakerLLM: A Speaker-Specialized Audio-LLM for Speaker Understanding and Verification Reasoning
KiHyun Nam, Jungwoo Heo, Siu Bae +2
As audio-first agents become increasingly common in physical AI, conversational robots, and screenless wearables, audio large language models (audio-LLMs) must integrate speaker-sp…
SV-Mixer: Replacing the Transformer Encoder with Lightweight MLPs for Self-Supervised Model Compression in Speaker Verification
Jungwoo Heo, Hyun-seo Shin, Chan-yeong Lim +4
Self-supervised learning (SSL) has pushed speaker verification accuracy close to state-of-the-art levels, but the Transformer backbones used in most SSL encoders hinder on-device a…
SEED: Speaker Embedding Enhancement Diffusion Model
KiHyun Nam, Jungwoo Heo, Jee-weon Jung +4
A primary challenge when deploying speaker recognition systems in real-world applications is performance degradation caused by environmental mismatch. We propose a diffusion-based…
MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms
Seung-bin Kim, Chan-yeong Lim, Jungwoo Heo +4
In speaker verification systems, the utilization of short utterances presents a persistent challenge, leading to performance degradation primarily due to insufficient phonetic info…
HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods
Hyun-seo Shin, Jungwoo Heo, Ju-ho Kim +3
Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems. Spoofing evidence, which helps to distinguish betw…
Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models
Ju-ho Kim, Jungwoo Heo, Hyun-seo Shin +2
Background noise considerably reduces the accuracy and reliability of speaker verification (SV) systems. These challenges can be addressed using a speech enhancement system as a fr…
PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification
Wonbin Kim, Hyun-seo Shin, Ju-ho Kim +3
Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV system…
One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification
Jungwoo Heo, Chan-yeong Lim, Ju-ho Kim +2
The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in e…
Convolution channel separation and frequency sub-bands aggregation for music genre classification
Jungwoo Heo, Hyun-seo Shin, Ju-ho Kim +2
In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able…
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
Ju-ho Kim, Jungwoo Heo, Hyun-seo Shin +2
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-ag…
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin
The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade. However, ASV systems can be easily neu…
Extended U-Net for Speaker Verification in Noisy Environments
Ju-ho Kim, Jungwoo Heo, Hye-jin Shim +1
Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility. Various studies have…
Graph attentive feature aggregation for text-independent speaker verification
Hye-jin Shim, Jungwoo Heo, Jae-han Park +2
The objective of this paper is to combine multiple frame-level features into a single utterance-level representation considering pairwise relationship. For this purpose, we propose…
RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies
Ju-ho Kim, Hye-jin Shim, Jungwoo Heo +1
Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of syst…