16 citations · 108 across the 36 of their papers we have counts for
24 papers · 1 filter
Token-based Attractors and Cross-attention in Spoof Diarization
Kyo-Won Koo, Chan-yeong Lim, Jee-weon Jung +2
Spoof diarization identifies ``what spoofed when" in a given speech by temporally locating spoofed regions and determining their manipulation techniques. As a first step toward thi…
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…
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
Xin Wang, Hector Delgado, Hemlata Tak +10
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previou…
To what extent can ASV systems naturally defend against spoofing attacks?
Jee-weon Jung, Xin Wang, Nicholas Evans +6
The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target. However, emerging advancements in speech gener…
a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
Hye-jin Shim, Jee-weon Jung, Tomi Kinnunen +3
Spoofing detection is today a mainstream research topic. Standard metrics can be applied to evaluate the performance of isolated spoofing detection solutions and others have been p…
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
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…
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…
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…
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
Jee-weon Jung, Hee-Soo Heo, Hemlata Tak +5
Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains. Their reliable detection usually depends upon computationally demanding e…
Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System
Ju-ho Kim, Hye-jin Shim, Jee-weon Jung +1
Most speaker verification tasks are studied as an open-set evaluation scenario considering the real-world condition. Thus, the generalization power to unseen speakers is of paramou…
DcaseNet: An integrated pretrained deep neural network for detecting and classifying acoustic scenes and events
Jee-weon Jung, Hye-jin Shim, Ju-ho Kim +1
Although acoustic scenes and events include many related tasks, their combined detection and classification have been scarcely investigated. We propose three architectures of deep…
Capturing scattered discriminative information using a deep architecture in acoustic scene classification
Hye-jin Shim, Jee-weon Jung, Ju-ho Kim +1
Frequently misclassified pairs of classes that share many common acoustic properties exist in acoustic scene classification (ASC). To distinguish such pairs of classes, trivial det…
Integrated Replay Spoofing-aware Text-independent Speaker Verification
Hye-jin Shim, Jee-weon Jung, Ju-ho Kim +2
A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary…
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
Jee-weon Jung, Seung-bin Kim, Hye-jin Shim +2
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms. For example, RawNet extracts speaker embeddings from…
Segment Aggregation for short utterances speaker verification using raw waveforms
Seung-bin Kim, Jee-weon Jung, Hye-jin Shim +2
Most studies on speaker verification systems focus on long-duration utterances, which are composed of sufficient phonetic information. However, the performances of these systems ar…
Acoustic Scene Classification using Audio Tagging
Jee-weon Jung, Hye-jin Shim, Ju-ho Kim +2
Acoustic scene classification systems using deep neural networks classify given recordings into pre-defined classes. In this study, we propose a novel scheme for acoustic scene cla…
A study on the role of subsidiary information in replay attack spoofing detection
Jee-weon Jung, Hye-jin Shim, Hee-Soo Heo +1
In this study, we analyze the role of various categories of subsidiary information in conducting replay attack spoofing detection: `Room Size', `Reverberation', `Speaker-to-ASV dis…
Acoustic scene classification using teacher-student learning with soft-labels
Hee-Soo Heo, Jee-weon Jung, Hye-jin Shim +1
Acoustic scene classification identifies an input segment into one of the pre-defined classes using spectral information. The spectral information of acoustic scenes may not be mut…
Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge
Jee-weon Jung, Hye-jin Shim, Hee-Soo Heo +1
In this study, we concentrate on replacing the process of extracting hand-crafted acoustic feature with end-to-end DNN using complementary high-resolution spectrograms. As a result…
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
Jee-weon Jung, Hee-Soo Heo, Ju-ho Kim +2
Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains. In speaker verification, however, utilization…
End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification
Hee-Soo Heo, Jee-weon Jung, IL-Ho Yang +3
In recent years, speaker verification has primarily performed using deep neural networks that are trained to output embeddings from input features such as spectrograms or Mel-filte…
Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
Jee-weon Jung, Hee-soo Heo, Hye-jin Shim +1
The short duration of an input utterance is one of the most critical threats that degrade the performance of speaker verification systems. This study aimed to develop an integrated…
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
Hye-Jin Shim, Jee-weon Jung, Hee-Soo Heo +2
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multitask learning of noise classes. We define the noise that is caused…