Showing eess.ASShow all
3 papers · 1 filter
eess.AS2024
Song Data Cleansing for End-to-End Neural Singer Diarization Using Neural Analysis and Synthesis Framework
Hokuto Munakata, Ryo Terashima, Yusuke Fujita
We propose a data cleansing method that utilizes a neural analysis and synthesis (NANSY++) framework to train an end-to-end neural diarization model (EEND) for singer diarization.…
eess.AS2022
Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis
Yuma Shirahata, Ryuichi Yamamoto, Eunwoo Song +3
Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models se…
eess.AS2022
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation
Ryo Terashima, Ryuichi Yamamoto, Eunwoo Song +4
Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are availabl…