12 papers · 1 filter
DialogueSidon: Recovering Full-Duplex Dialogue Tracks from In-the-Wild Dialogue Audio
Wataru Nakata, Yuki Saito, Kazuki Yamauchi +2
Full-duplex dialogue audio, in which each speaker is recorded on a separate track, is an important resource for spoken dialogue research, but is difficult to collect at scale. Most…
Geneses: Unified Generative Speech Enhancement and Separation
Kohei Asai, Wataru Nakata, Yuki Saito +1
Real-world audio recordings often contain multiple speakers and various degradations, which limit both the quantity and quality of speech data available for building state-of-the-a…
DistilMOS: Layer-Wise Self-Distillation For Self-Supervised Learning Model-Based MOS Prediction
Jianing Yang, Wataru Nakata, Yuki Saito +1
With the advancement of self-supervised learning (SSL), fine-tuning pretrained SSL models for mean opinion score (MOS) prediction has achieved state-of-the-art performance. However…
Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing
Wataru Nakata, Yuki Saito, Yota Ueda +1
Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, multilingual recordings. We introduce Sidon, a fast, open-source speech restoration model that conver…
Multi-Sampling-Frequency Naturalness MOS Prediction Using Self-Supervised Learning Model with Sampling-Frequency-Independent Layer
Go Nishikawa, Wataru Nakata, Yuki Saito +3
We introduce our submission to the AudioMOS Challenge (AMC) 2025 Track 3: mean opinion score (MOS) prediction for speech with multiple sampling frequencies (SFs). Our submitted mod…
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…
The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech
Kaito Baba, Wataru Nakata, Yuki Saito +1
We present our system (denoted as T05) for the VoiceMOS Challenge (VMC) 2024. Our system was designed for the VMC 2024 Track 1, which focused on the accurate prediction of naturaln…
UTDUSS: UTokyo-SaruLab System for Interspeech2024 Speech Processing Using Discrete Speech Unit Challenge
Wataru Nakata, Kazuki Yamauchi, Dong Yang +2
We present UTDUSS, the UTokyo-SaruLab system submitted to Interspeech2024 Speech Processing Using Discrete Speech Unit Challenge. The challenge focuses on using discrete speech uni…
Building speech corpus with diverse voice characteristics for its prompt-based representation
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +3
In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it shou…
Coco-Nut: Corpus of Japanese Utterance and Voice Characteristics Description for Prompt-based Control
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +3
In text-to-speech, controlling voice characteristics is important in achieving various-purpose speech synthesis. Considering the success of text-conditioned generation, such as tex…
UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022
Takaaki Saeki, Detai Xin, Wataru Nakata +3
We present the UTokyo-SaruLab mean opinion score (MOS) prediction system submitted to VoiceMOS Challenge 2022. The challenge is to predict the MOS values of speech samples collecte…
J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis
Shinnosuke Takamichi, Wataru Nakata, Naoko Tanji +1
In this paper, we construct a Japanese audiobook speech corpus called "J-MAC" for speech synthesis research. With the success of reading-style speech synthesis, the research target…