9 papers
Investigating training objective for flow matching-based speech enhancement
Liusha Yang, Ziru Ge, Gui Zhang +2
Speech enhancement(SE) aims to recover clean speech from noisy recordings. Although generative approaches such as score matching and Schrodinger bridge have shown strong effectiven…
AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck
Junan Zhang, Yunjia Zhang, Xueyao Zhang +1
Singing Accompaniment Generation (SAG) is the process of generating instrumental music for a given clean vocal input. However, existing SAG techniques use source-separated vocals a…
The CCF AATC 2025 Speech Restoration Challenge: A Retrospective
Junan Zhang, Mengyao Zhu, Xin Xu +3
Real-world speech communication is rarely affected by a single type of degradation. Instead, it suffers from a complex interplay of acoustic interference, codec compression, and, i…
TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling
Yuancheng Wang, Dekun Chen, Xueyao Zhang +3
Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vec…
Multi-Metric Preference Alignment for Generative Speech Restoration
Junan Zhang, Xueyao Zhang, Jing Yang +3
Recent generative models have significantly advanced speech restoration tasks, yet their training objectives often misalign with human perceptual preferences, resulting in suboptim…
SingNet: Towards a Large-Scale, Diverse, and In-the-Wild Singing Voice Dataset
Yicheng Gu, Chaoren Wang, Junan Zhang +4
The lack of a publicly-available large-scale and diverse dataset has long been a significant bottleneck for singing voice applications like Singing Voice Synthesis (SVS) and Singin…