24 citations · 37 across the 11 of their papers we have counts for
11 papers
Cross-speaker Emotion Transfer by Manipulating Speech Style Latents
Suhee Jo, Younggun Lee, Yookyung Shin +2
In recent years, emotional text-to-speech has shown considerable progress. However, it requires a large amount of labeled data, which is not easily accessible. Even if it is possib…
Hi,KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
Taesu Kim, SeungHeon Doh, Gyunpyo Lee +3
Wake-up words (WUW) is a short sentence used to activate a speech recognition system to receive the user's speech input. WUW utterances include not only the lexical information for…
Affective Role of the Future Autonomous Vehicle Interior
Taesu Kim, Gyunpyo Lee, Jiwoo Hong +1
Recent advancements in autonomous technology allow for new opportunities in vehicle interior design. Such a shift in in-vehicle activity suggests vehicle interior spaces should pro…
Affective responses to chromatic ambient light in a vehicle
Taesu Kim, Kyungah Choi, Hyeon-Jeong Suk
This study investigates the emotional responses to the color of vehicle interior lighting using self-assessment and electroencephalography (EEG). The study was divided into two ses…
Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS
Yookyung Shin, Younggun Lee, Suhee Jo +2
Expressive text-to-speech has shown improved performance in recent years. However, the style control of synthetic speech is often restricted to discrete emotion categories and requ…
GP22: A Car Styling Dataset for Automotive Designers
Gyunpyo Lee, Taesu Kim, Hyeon-Jeong Suk
An automated design data archiving could reduce the time wasted by designers from working creatively and effectively. Though many datasets on classifying, detecting, and instance s…
EdiTTS: Score-based Editing for Controllable Text-to-Speech
Jaesung Tae, Hyeongju Kim, Taesu Kim
We present EdiTTS, an off-the-shelf speech editing methodology based on score-based generative modeling for text-to-speech synthesis. EdiTTS allows for targeted, granular editing o…
Large-scale Speaker Retrieval on Random Speaker Variability Subspace
Suwon Shon, Younggun Lee, Taesu Kim
This paper describes a fast speaker search system to retrieve segments of the same voice identity in the large-scale data. A recent study shows that Locality Sensitive Hashing (LSH…
Learning pronunciation from a foreign language in speech synthesis networks
Younggun Lee, Suwon Shon, Taesu Kim
Although there are more than 6,500 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronu…
Robust and fine-grained prosody control of end-to-end speech synthesis
Younggun Lee, Taesu Kim
We propose prosody embeddings for emotional and expressive speech synthesis networks. The proposed methods introduce temporal structures in the embedding networks, thus enabling fi…
Voice Imitating Text-to-Speech Neural Networks
Younggun Lee, Taesu Kim, Soo-Young Lee
We propose a neural text-to-speech (TTS) model that can imitate a new speaker's voice using only a small amount of speech sample. We demonstrate voice imitation using only a 6-seco…