2 citations · 4 across the 7 of their papers we have counts for
8 papers
Mitigating Intra-Speaker Variability in Diarization with Style-Controllable Speech Augmentation
Miseul Kim, Soo Jin Park, Kyungguen Byun +4
Speaker diarization systems often struggle with high intrinsic intra-speaker variability, such as shifts in emotion, health, or content. This can cause segments from the same speak…
Voice-ENHANCE: Speech Restoration using a Diffusion-based Voice Conversion Framework
Kyungguen Byun, Jason Filos, Erik Visser +1
We propose a speech enhancement system that combines speaker-agnostic speech restoration with voice conversion (VC) to obtain a studio-level quality speech signal. While voice conv…
VC-ENHANCE: Speech Restoration with Integrated Noise Suppression and Voice Conversion
Kyungguen Byun, Jason Filos, Erik Visser +1
Noise suppression (NS) algorithms are effective in improving speech quality in many cases. However, aggressive noise suppression can damage the target speech, reducing both speech…
Highly Controllable Diffusion-based Any-to-Any Voice Conversion Model with Frame-level Prosody Feature
Kyungguen Byun, Sunkuk Moon, Erik Visser
We propose a highly controllable voice manipulation system that can perform any-to-any voice conversion (VC) and prosody modulation simultaneously. State-of-the-art VC systems can…
Stylebook: Content-Dependent Speaking Style Modeling for Any-to-Any Voice Conversion using Only Speech Data
Hyungseob Lim, Kyungguen Byun, Sunkuk Moon +1
While many recent any-to-any voice conversion models succeed in transferring some target speech's style information to the converted speech, they still lack the ability to faithful…
Emotional speech synthesis with rich and granularized control
Se-Yun Um, Sangshin Oh, Kyungguen Byun +3
This paper proposes an effective emotion control method for an end-to-end text-to-speech (TTS) system. To flexibly control the distinct characteristic of a target emotion category,…
ExcitNet vocoder: A neural excitation model for parametric speech synthesis systems
Eunwoo Song, Kyungguen Byun, Hong-Goo Kang
This paper proposes a WaveNet-based neural excitation model (ExcitNet) for statistical parametric speech synthesis systems. Conventional WaveNet-based neural vocoding systems signi…
Speaker-adaptive neural vocoders for parametric speech synthesis systems
Eunwoo Song, Jin-Seob Kim, Kyungguen Byun +1
This paper proposes speaker-adaptive neural vocoders for parametric text-to-speech (TTS) systems. Recently proposed WaveNet-based neural vocoding systems successfully generate a ti…