184 citations · 222 across the 16 of their papers we have counts for
18 papers
ReverbMiipher: Generative Speech Restoration meets Reverberation Characteristics Controllability
Wataru Nakata, Yuma Koizumi, Shigeki Karita +5
Reverberation encodes spatial information regarding the acoustic source environment, yet traditional Speech Restoration (SR) usually completely removes reverberation. We propose Re…
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration
Shigeki Karita, Yuma Koizumi, Heiga Zen +3
Training data cleaning is a new application for generative model-based speech restoration (SR). This paper introduces Miipher-2, an SR model designed for million-hour scale data, f…
Adaptive Dropout for Pruning Conformers
Yotaro Kubo, Xingyu Cai, Michiel Bacchiani
This paper proposes a method to effectively perform joint training-and-pruning based on adaptive dropout layers with unit-wise retention probabilities. The proposed method is based…
FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
Min Ma, Yuma Koizumi, Shigeki Karita +4
This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R maintains…
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which c…
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a n…