77 citations · 83 across the 5 of their papers we have counts for
6 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…
StyLEx: Explaining Style Using Human Lexical Annotations
Shirley Anugrah Hayati, Kyumin Park, Dheeraj Rajagopal +2
Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (H…
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…
SSMix: Saliency-Based Span Mixup for Text Classification
Soyoung Yoon, Gyuwan Kim, Kyumin Park
Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since text c…
KLUE: Korean Language Understanding Evaluation
Sungjoon Park, Jihyung Moon, Sungdong Kim +28
We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…
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…