5 papers
ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
Sang-Hoon Lee, Ha-Yeong Choi
Representation alignment (REPA) has been investigated to accelerate diffusion training, but we observe that regularizing intermediate representations in diffusion Transformers (DiT…
DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignment
Hyung-Seok Oh, Sang-Hoon Lee, Deok-Hyeon Cho +1
Emotional voice conversion (EVC) involves modifying various acoustic characteristics, such as pitch and spectral envelope, to match a desired emotional state while preserving the s…
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…
Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization
Sang-Hoon Lee, Ha-Yeong Choi, Seong-Whan Lee
This paper introduces PeriodWave-Turbo, a high-fidelity and high-efficient waveform generation model via adversarial flow matching optimization. Recently, conditional flow matching…
PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform Generation
Sang-Hoon Lee, Ha-Yeong Choi, Seong-Whan Lee
Recently, universal waveform generation tasks have been investigated conditioned on various out-of-distribution scenarios. Although GAN-based methods have shown their strength in f…