Graduate students

Seongyeop Jeong

PhD student (21.03 ~ )

Seongyeop Jeong received his M.S. degree in electrical engineering and computer science from the Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea, in 2021. He is currently pursuing a Ph.D. degree at the Kim Jaechul Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.

Seongyeop Jeong received his M.S. degree in electrical engineering and computer science from the Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea, in 2021. He is currently pursuing a Ph.D. degree at the Kim Jaechul Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Research interest: Speech synthesis Synthesis error correction via XAI analysis
seongyeop.jeong (at) kaist.ac.kr

3 notes

Research

All research

2025
Context-aware Speech Correction
Automatic detection and correction of abnormal encoder context vectors improves synthesized speech without extra data or model retraining.
2025
An Automated Method to Correct Artifacts in Neural Text-to-Speech Models
Recent advances in deep learning technology have enabled high-quality speech synthesis, and text-to-speech models are widely used in a variety of applications. However, even stateof-the-art models still produce artificial speech, highlighting the need to correct errors in synthesized speech. Traditional speech correction methodologies face a number of challenges, such as the inefficiency of manually specifying errors, the need to retrain models, and the need for additional data to correct synthesized speech errors. In this paper, we present a novel approach that detects and corrects contextual errors within a model to improve synthetic speech without requiring additional resources or model retraining. Specifically, we analyze the inherent limitations of neural network encoders responsible for contextualizing input sentences and propose a method that automatically identifies abnormal encoder context vectors. We also introduce a correction algorithm that enhances the quality of speech by correcting the incorrect relationships between phonemes that cause abnormal encoder context vectors. Our algorithm demonstrated a 25.86% and 4.69% reduction in alignment errors and a 2.25% and 0.63% improvement in objective metrics such as Frechet Wav2Vec ´ distance for the Tacotron2 and VITS models. In addition, our algorithm improved the comparative mean opinion scores, a subjective evaluation, by 1.34 and 0.52. The results support the feasibility of a novel approach to identify, correct, and improve defects automatically in neural speech synthesis models. The results support the feasibility of a novel approach to identify, correct, and improve defects automatically in neural speech synthesis models.
2023
Explainable AI-Based Interface System for Weather Forecasting Model
As machine learning (ML) becomes more prevalent in meteorological decision-making, user-centered explainable artificial intelligence (XAI) studies have yet to extend into this domain. This study identifies three key requirements for explaining black-box models in meteorology: model bias in different rainfall scenarios, model reasoning, and output confidence. Appropriate XAI methods are mapped to these requirements and evaluated both quantitatively and qualitatively. The results show that intuitive explanations improve decision-making utility and user trust, highlighting the need for user-centered XAI to enhance the usability of AI systems in practice.