27 citations · 55 across the 12 of their papers we have counts for
12 papers
L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification
Hyung-Seok Oh, Deok-Hyeon Cho, Seung-Bin Kim +1
Multilingual speaker verification remains challenging because language-dependent acoustic variability causes speaker identity to become entangled with linguistic characteristics, d…
Toward Complex-Valued Neural Networks for Waveform Generation
Hyung-Seok Oh, Deok-Hyeon Cho, Seung-Bin Kim +1
Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They…
ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
Xiaofeng Lin, Seungbae Kim, Zhuoya Li +3
Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the c…
Spotlight-TTS: Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speech
Nam-Gyu Kim, Deok-Hyeon Cho, Seung-Bin Kim +1
Recent advances in expressive text-to-speech (TTS) have introduced diverse methods based on style embedding extracted from reference speech. However, synthesizing high-quality expr…
EmoSphere-SER: Enhancing Speech Emotion Recognition Through Spherical Representation with Auxiliary Classification
Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim +1
Speech emotion recognition predicts a speaker's emotional state from speech signals using discrete labels or continuous dimensions such as arousal, valence, and dominance (VAD). We…
DiEmo-TTS: Disentangled Emotion Representations via Self-Supervised Distillation for Cross-Speaker Emotion Transfer in Text-to-Speech
Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim +1
Cross-speaker emotion transfer in speech synthesis relies on extracting speaker-independent emotion embeddings for accurate emotion modeling without retaining speaker traits. Howev…
FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow Matching
Jun-Hak Yun, Seung-Bin Kim, Seong-Whan Lee
Audio super-resolution is challenging owing to its ill-posed nature. Recently, the application of diffusion models in audio super-resolution has shown promising results in alleviat…
JELLY: Joint Emotion Recognition and Context Reasoning with LLMs for Conversational Speech Synthesis
Jun-Hyeok Cha, Seung-Bin Kim, Hyung-Seok Oh +1
Recently, there has been a growing demand for conversational speech synthesis (CSS) that generates more natural speech by considering the conversational context. To address this, w…
EmoSphere++: Emotion-Controllable Zero-Shot Text-to-Speech via Emotion-Adaptive Spherical Vector
Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim +1
Emotional text-to-speech (TTS) technology has achieved significant progress in recent years; however, challenges remain owing to the inherent complexity of emotions and limitations…
EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech
Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim +2
Despite rapid advances in the field of emotional text-to-speech (TTS), recent studies primarily focus on mimicking the average style of a particular emotion. As a result, the abili…
TranSentence: Speech-to-speech Translation via Language-agnostic Sentence-level Speech Encoding without Language-parallel Data
Seung-Bin Kim, Sang-Hoon Lee, Seong-Whan Lee
Although there has been significant advancement in the field of speech-to-speech translation, conventional models still require language-parallel speech data between the source and…
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis
Sang-Hoon Lee, Ha-Yeong Choi, Seung-Bin Kim +1
Large language models (LLM)-based speech synthesis has been widely adopted in zero-shot speech synthesis. However, they require a large-scale data and possess the same limitations…