6 papers
RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching
Jinhyeok Yang, Hyeongju Kim, Yechan Yu +3
While flow-matching text-to-speech (TTS) achieves strong zero-shot speaker similarity and naturalness, it remains susceptible to content fidelity issues, particularly skip and repe…
Robust TTS Training via Self-Purifying Flow Matching for the WildSpoof 2026 TTS Track
June Young Yi, Hyeongju Kim, Juheon Lee
This paper presents a lightweight text-to-speech (TTS) system developed for the WildSpoof Challenge TTS Track. Our approach fine-tunes the recently released open-weight TTS model,…
Improving Test-Time Performance of RVQ-based Neural Codecs
Hyeongju Kim, Junhyeok Lee, Jacob Morton +2
The residual vector quantization (RVQ) technique plays a central role in recent advances in neural audio codecs. These models effectively synthesize high-fidelity audio from a limi…
Training Flow Matching Models with Reliable Labels via Self-Purification
Hyeongju Kim, Yechan Yu, June Young Yi +1
Training datasets are inherently imperfect, often containing mislabeled samples due to human annotation errors, limitations of tagging models, and other sources of noise. Such labe…
SupertonicTTS: Towards Highly Efficient and Streamlined Text-to-Speech System
Hyeongju Kim, Jinhyeok Yang, Yechan Yu +5
We introduce SupertonicTTS, a novel text-to-speech (TTS) system designed for efficient and streamlined speech synthesis. SupertonicTTS comprises three components: a speech autoenco…
Length-Aware Rotary Position Embedding for Text-Speech Alignment
Hyeongju Kim, Juheon Lee, Jinhyeok Yang +1
Many recent text-to-speech (TTS) systems are built on transformer architectures and employ cross-attention mechanisms for text-speech alignment. Within these systems, rotary positi…