3 papers
cs.CV2025
Video Consistency Distance: Enhancing Temporal Consistency for Image-to-Video Generation via Reward-Based Fine-Tuning
Takehiro Aoshima, Yusuke Shinohara, Byeongseon Park
Reward-based fine-tuning of video diffusion models is an effective approach to improve the quality of generated videos, as it can fine-tune models without requiring real-world vide…
cs.SD2025
Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
Hien Ohnaka, Yuma Shirahata, Byeongseon Park +1
We propose a model to obtain phonemic and prosodic labels of speech that are coherent with graphemes. Unlike previous methods that simply fine-tune a pre-trained ASR model with the…
eess.AS2024
Audio-conditioned phonemic and prosodic annotation for building text-to-speech models from unlabeled speech data
Yuma Shirahata, Byeongseon Park, Ryuichi Yamamoto +1
This paper proposes an audio-conditioned phonemic and prosodic annotation model for building text-to-speech (TTS) datasets from unlabeled speech samples. For creating a TTS dataset…