5 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…
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…
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…
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…
DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance
Jinhyeok Yang, Junhyeok Lee, Hyeong-Seok Choi +3
Text-to-Speech (TTS) models have advanced significantly, aiming to accurately replicate human speech's diversity, including unique speaker identities and linguistic nuances. Despit…