14 citations · 20 across the 17 of their papers we have counts for
18 papers
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…
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
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…
ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech
Jiatong Shi, Jinchuan Tian, Yihan Wu +17
Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance do…
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…
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
Yongyi Zang, Jiatong Shi, You Zhang +8
Recent singing voice synthesis and conversion advancements necessitate robust singing voice deepfake detection (SVDD) models. Current SVDD datasets face challenges due to limited c…
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…
SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
You Zhang, Yongyi Zang, Jiatong Shi +5
The rapid advancement of AI-generated singing voices, which now closely mimic natural human singing and align seamlessly with musical scores, has led to heightened concerns for art…
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…