2 citations · 3 across the 14 of their papers we have counts for
16 papers
Articulatory Source-Filter TTS: Physically Grounded Control through Vocal Tract Kinematics
Jesuraj Bandekar, Shinji Watanabe, Prasanta Kumar Ghosh
Modern neural text-to-speech (TTS) systems achieve remarkable acoustic fidelity but act as black boxes, offering little interpretable control over the vocal tract filter. We propos…
HaikuS2S: A Cascaded System For Responding In Verse
Devangi Sharma, Sophia Judicke, Glenda Tan +2
Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior work on prosody transfer impro…
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…
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…
MMM: Multi-Layer Multi-Residual Multi-Stream Discrete Speech Representation from Self-supervised Learning Model
Jiatong Shi, Xutai Ma, Hirofumi Inaguma +2
Speech discrete representation has proven effective in various downstream applications due to its superior compression rate of the waveform, fast convergence during training, and 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…
VISinger2+: End-to-End Singing Voice Synthesis Augmented by Self-Supervised Learning Representation
Yifeng Yu, Jiatong Shi, Yuning Wu +2
Singing Voice Synthesis (SVS) has witnessed significant advancements with the advent of deep learning techniques. However, a significant challenge in SVS is the scarcity of labeled…
A Large-Scale Evaluation of Speech Foundation Models
Shu-wen Yang, Heng-Jui Chang, Zili Huang +18
The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling a…
LV-CTC: Non-autoregressive ASR with CTC and latent variable models
Yuya Fujita, Shinji Watanabe, Xuankai Chang +1
Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of…
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…
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model
Takashi Maekaku, Jiatong Shi, Xuankai Chang +2
Recently, the usefulness of self-supervised representation learning (SSRL) methods has been confirmed in various downstream tasks. Many of these models, as exemplified by HuBERT an…
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…
EFFUSE: Efficient Self-Supervised Feature Fusion for E2E ASR in Low Resource and Multilingual Scenarios
Tejes Srivastava, Jiatong Shi, William Chen +1
Self-Supervised Learning (SSL) models have demonstrated exceptional performance in various speech tasks, particularly in low-resource and multilingual domains. Recent works show th…