1 citations · 2 across the 9 of their papers we have counts for
9 papers
Vec-Tok-VC+: Residual-enhanced Robust Zero-shot Voice Conversion with Progressive Constraints in a Dual-mode Training Strategy
Linhan Ma, Xinfa Zhu, Yuanjun Lv +5
Zero-shot voice conversion (VC) aims to transform source speech into arbitrary unseen target voice while keeping the linguistic content unchanged. Recent VC methods have made signi…
FreeV: Free Lunch For Vocoders Through Pseudo Inversed Mel Filter
Yuanjun Lv, Hai Li, Ying Yan +3
Vocoders reconstruct speech waveforms from acoustic features and play a pivotal role in modern TTS systems. Frequent-domain GAN vocoders like Vocos and APNet2 have recently seen ra…
RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
In real-time speech communication systems, speech signals are often degraded by multiple distortions. Recently, a two-stage Repair-and-Denoising network (RaD-Net) was proposed with…
Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
Hanzhao Li, Liumeng Xue, Haohan Guo +6
The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction. To avoid t…
AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
Qian Yang, Jin Xu, Wenrui Liu +8
Recently, instruction-following audio-language models have received broad attention for human-audio interaction. However, the absence of benchmarks capable of evaluating audio-cent…
RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
This paper introduces our repairing and denoising network (RaD-Net) for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. We extend our previous framework based on a two-s…
SELM: Speech Enhancement Using Discrete Tokens and Language Models
Ziqian Wang, Xinfa Zhu, Zihan Zhang +4
Language models (LMs) have shown superior performances in various speech generation tasks recently, demonstrating their powerful ability for semantic context modeling. Given the in…
Vec-Tok Speech: speech vectorization and tokenization for neural speech generation
Xinfa Zhu, Yuanjun Lv, Yi Lei +5
Language models (LMs) have recently flourished in natural language processing and computer vision, generating high-fidelity texts or images in various tasks. In contrast, the curre…
SALT: Distinguishable Speaker Anonymization Through Latent Space Transformation
Yuanjun Lv, Jixun Yao, Peikun Chen +3
Speaker anonymization aims to conceal a speaker's identity without degrading speech quality and intelligibility. Most speaker anonymization systems disentangle the speaker represen…