3 citations · 4 across the 19 of their papers we have counts for
9 papers · 1 filter
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies
Sangmin Lee, Yejin Jeon, David Ifeoluwa Adelani
Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresent much of the linguistic long…
OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages
David Guzmán, Luel Hagos Beyene, Jesujoba Oluwadara Alabi +3
Recent advances in neural text-to-speech (TTS) and multilingual speech generation have substantially improved synthetic speech quality, yet these gains remain unevenly distributed…
Difficulty-Controllable Cloze Question Distractor Generation
Seokhoon Kang, Yejin Jeon, Seonjeong Hwang +1
Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors remains challenging, as existing…
Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection
San Kim, Jonghwi Kim, Yejin Jeon +1
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by providing external knowledge for accurate and up-to-date responses. However, this reliance on external…
EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance
Heejae Suh, Yejin Jeon, Deokhyung Kang +3
Small large language models (sLLMs) offer the advantage of being lightweight and efficient, which makes them suitable for resource-constrained environments. However, sLLMs often st…
PicPersona-TOD : A Dataset for Personalizing Utterance Style in Task-Oriented Dialogue with Image Persona
Jihyun Lee, Yejin Jeon, Seungyeon Seo +1
Task-Oriented Dialogue (TOD) systems are designed to fulfill user requests through natural language interactions, yet existing systems often produce generic, monotonic responses th…
Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation
Deokhyung Kang, Jeonghun Cho, Yejin Jeon +4
Visual programming languages (VPLs) allow users to create programs through graphical interfaces, which results in easier accessibility and their widespread usage in various domains…
An Investigation Into Explainable Audio Hate Speech Detection
Jinmyeong An, Wonjun Lee, Yejin Jeon +3
Research on hate speech has predominantly revolved around detection and interpretation from textual inputs, leaving verbal content largely unexplored. While there has been limited…
Leveraging the Interplay Between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation
Yejin Jeon, Yunsu Kim, Gary Geunbae Lee
Contemporary neural speech synthesis models have indeed demonstrated remarkable proficiency in synthetic speech generation as they have attained a level of quality comparable to th…