53 citations
- Seoul National UniversityKR2 papers
- Yonsei UniversityKR2 papers
- Electronics and Telecommunications Research InstituteKR1 paper
- Gwangju Institute of Science and TechnologyKR1 paper
- Korea UniversityKR1 paper
- Sejong UniversityKR1 paper
- Seoul National University Bundang HospitalKR1 paper
- Soonchunhyang UniversityKR1 paper
- Sungkyunkwan UniversityKR1 paper
- University of EdinburghGB1 paper
4 papers · 1 filter
Varianceflow: High-Quality and Controllable Text-to-Speech using Variance Information via Normalizing Flow
Yoonhyung Lee, Jinhyeok Yang, Kyomin Jung
There are two types of methods for non-autoregressive text-to-speech models to learn the one-to-many relationship between text and speech effectively. The first one is to use an ad…
Hierarchical Context-Aware Transformers for Non-Autoregressive Text to Speech
Jae-Sung Bae, Tae-Jun Bak, Young-Sun Joo +1
In this paper, we propose methods for improving the modeling performance of a Transformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although the text encoder and aud…
A Neural Text-to-Speech Model Utilizing Broadcast Data Mixed with Background Music
Hanbin Bae, Jae-Sung Bae, Young-Sun Joo +2
Recently, it has become easier to obtain speech data from various media such as the internet or YouTube, but directly utilizing them to train a neural text-to-speech (TTS) model is…
Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking
Yoonjae Jeong, Hoon-Young Cho
The purpose of this study is to detect the mismatch between text script and voice-over. For this, we present a novel utterance verification (UV) method, which calculates the degree…