158 citations · 303 across the 76 of their papers we have counts for
8 papers · 2 filters
What Actually Makes Correlation-Based SSL Distillation Noise-Robust? A Mechanistic Correction
Fabian Ritter-Gutierrez, Nancy F. Chen, Eng Siong Chng
Self-supervised learning (SSL) speech models are accurate but large. Knowledge distillation compresses them, but the student loses the teacher's noise robustness. Correlation-based…
AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing
Ziyang Ma, Zhikang Niu, Wenming Tu +30
We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To sup…
VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition
Yukun Chen, Tianrui Wang, Zhaoxi Mu +2
Existing singing voice synthesis systems often require predefined durations, explicit duration prediction, or time-aligned acoustic guidance, which limits their compatibility with…
Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
Yue Heng Yeo, Haoyang Li, Yizhou Peng +6
Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training. Although synthetic data augme…
MMAE: A Massive Multitask Audio Editing Benchmark
Ziyang Ma, Ruiqi Yan, Ruiyang Xu +35
We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing.…
VocalParse: Towards Unified and Scalable Singing Voice Transcription with Large Audio Language Models
Yukun Chen, Tianrui Wang, Zhaoxi Mu +2
High-quality singing annotations are fundamental to modern Singing Voice Synthesis (SVS) systems. However, obtaining these annotations at scale through manual labeling is unrealist…