3 citations · 5 across the 5 of their papers we have counts for
5 papers
RedPen: Region- and Reason-Annotated Dataset of Unnatural Speech
Kyumin Park, Keon Lee, Daeyoung Kim +1
Even with recent advances in speech synthesis models, the evaluation of such models is based purely on human judgement as a single naturalness score, such as the Mean Opinion Score…
DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
Keon Lee, Kyumin Park, Daeyoung Kim
The majority of current Text-to-Speech (TTS) datasets, which are collections of individual utterances, contain few conversational aspects. In this paper, we introduce DailyTalk, a…
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
Keon-Woo Lee, Dae-Hyeok Lee, Sung-Jin Kim +1
Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface ha…
Decoding High-level Imagined Speech using Attention-based Deep Neural Networks
Dae-Hyeok Lee, Sung-Jin Kim, Keon-Woo Lee
Brain-computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting status and intentions of humans. When conducting imagined s…
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech
Keon Lee, Kyumin Park, Daeyoung Kim
Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, an…