14 papers
DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modeling
Wataru Nakata, Yuki Saito, Hiroshi Saruwatari
Full-duplex spoken dialogue models are trained on conversational speech in which each speaker is represented as a separate stream, but existing large-scale public speech corpora ar…
Neural Audio Codec with Adjustable Token Temporal Resolution Using Sampling-Frequency-Independent Convolutional Layers
Tomohiko Nakamura, Wataru Nakata, Kanami Imamura +1
Discrete tokens obtained from neural audio codecs (NACs) have been used as compact representations in audio generation and understanding models. In such token-based systems, token…
Probing Token Spaces under Generator Shift in AI-Generated Music Detection
Joonyong Park, Jungwoo Kim, Junyoung Koh +1
AI-generated music detectors can appear robust on standard benchmark splits, yet their deployments require transfer to generator sources absent during training. We study this probl…
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…
Emotional Text-To-Speech Based on Mutual-Information-Guided Emotion-Timbre Disentanglement
Jianing Yang, Sheng Li, Takahiro Shinozaki +2
Current emotional Text-To-Speech (TTS) and style transfer methods rely on reference encoders to control global style or emotion vectors, but do not capture nuanced acoustic details…
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…
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features
Emiru Tsunoo, Yuki Saito, Wataru Nakata +1
Real-time speech enhancement (SE) is essential to online speech communication. Causal SE models use only the previous context while predicting future information, such as phoneme c…
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…
Cross-Dialect Text-To-Speech in Pitch-Accent Language Incorporating Multi-Dialect Phoneme-Level BERT
Kazuki Yamauchi, Yuki Saito, Hiroshi Saruwatari
We explore cross-dialect text-to-speech (CD-TTS), a task to synthesize learned speakers' voices in non-native dialects, especially in pitch-accent languages. CD-TTS is important fo…
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…
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models
Kazuki Yamauchi, Yusuke Ijima, Yuki Saito
We propose StyleCap, a method to generate natural language descriptions of speaking styles appearing in speech. Although most of conventional techniques for para-/non-linguistic in…