14 citations · 20 across the 17 of their papers we have counts for
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MMGenre: Benchmarking Singing Voice Synthesis across Multiple Musical Genres
Wenhao Feng, Yuxun Tang, Jiatong Shi +1
Singing voice synthesis (SVS) has progressed rapidly, yet its ability to generalize across diverse musical genres remains underexplored. Existing benchmarks are heavily biased towa…
SingingSDS: A Singing-Capable Spoken Dialogue System for Conversational Roleplay Applications
Jionghao Han, Jiatong Shi, Masao Someki +5
With recent advances in automatic speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) technologies, spoken dialogue systems (SDS) have become widely ac…
Adapting Speech Language Model to Singing Voice Synthesis
Yiwen Zhao, Jiatong Shi, Jinchuan Tian +4
Speech Language Models (SLMs) have recently emerged as a unified paradigm for addressing a wide range of speech-related tasks, including text-to-speech (TTS), speech enhancement (S…
Robust Training of Singing Voice Synthesis Using Prior and Posterior Uncertainty
Yiwen Zhao, Jiatong Shi, Yuxun Tang +2
Singing voice synthesis (SVS) has seen remarkable advancements in recent years. However, compared to speech and general audio data, publicly available singing datasets remain limit…
CartoonSing: Unifying Human and Nonhuman Timbres in Singing Generation
Jionghao Han, Jiatong Shi, Zhuoyan Tao +4
Singing voice synthesis (SVS) and singing voice conversion (SVC) have achieved remarkable progress in generating natural-sounding human singing. However, existing systems are restr…
SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment
Yuxun Tang, Lan Liu, Wenhao Feng +5
Singing voice generation progresses rapidly, yet evaluating singing quality remains a critical challenge. Human subjective assessment, typically in the form of listening tests, is…
VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Music
Jiatong Shi, Hye-jin Shim, Jinchuan Tian +14
In this work, we introduce VERSA, a unified and standardized evaluation toolkit designed for various speech, audio, and music signals. The toolkit features a Pythonic interface wit…
Muskits-ESPnet: A Comprehensive Toolkit for Singing Voice Synthesis in New Paradigm
Yuning Wu, Jiatong Shi, Yifeng Yu +7
This research presents Muskits-ESPnet, a versatile toolkit that introduces new paradigms to Singing Voice Synthesis (SVS) through the application of pretrained audio models in both…
SingMOS: An extensive Open-Source Singing Voice Dataset for MOS Prediction
Yuxun Tang, Jiatong Shi, Yuning Wu +1
In speech generation tasks, human subjective ratings, usually referred to as the opinion score, are considered the "gold standard" for speech quality evaluation, with the mean opin…
SingOMD: Singing Oriented Multi-resolution Discrete Representation Construction from Speech Models
Yuxun Tang, Yuning Wu, Jiatong Shi +1
Discrete representation has shown advantages in speech generation tasks, wherein discrete tokens are derived by discretizing hidden features from self-supervised learning (SSL) pre…
TokSing: Singing Voice Synthesis based on Discrete Tokens
Yuning Wu, Chunlei zhang, Jiatong Shi +3
Recent advancements in speech synthesis witness significant benefits by leveraging discrete tokens extracted from self-supervised learning (SSL) models. Discrete tokens offer highe…
The Interspeech 2024 Challenge on Speech Processing Using Discrete Units
Xuankai Chang, Jiatong Shi, Jinchuan Tian +7
Representing speech and audio signals in discrete units has become a compelling alternative to traditional high-dimensional feature vectors. Numerous studies have highlighted the e…
Singing Voice Data Scaling-up: An Introduction to ACE-Opencpop and ACE-KiSing
Jiatong Shi, Yueqian Lin, Xinyi Bai +6
In singing voice synthesis (SVS), generating singing voices from musical scores faces challenges due to limited data availability. This study proposes a unique strategy to address…
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
Jiatong Shi, William Chen, Dan Berrebbi +10
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual s…