5 papers
Phoenix TTS: High-Fidelity Synthesis and Voice Conversion via Flow-Matching-Driven Speech Tokenization
Peijie Chen, Zhuanling Zha, Zhipeng Nie +8
In current zero-shot text-to-speech systems, conventional semantic tokenizers are typically optimized using supervised automatic speech recognition or self-supervised learning obje…
MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning
Weijie Wu, Junbo Li, Lin Li +2
With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions. E…
SARA: A Dual-Stream VAE for High-Fidelity Speech Generation via Integrating Semantic and Acoustic Representations
Peijie Chen, Wenhao Guan, Weijie Wu +7
Zero-shot text-to-speech (TTS) relies on robust speech representations. However, current speech tokenizers face a fundamental trade-off: acoustic codecs preserve high-fidelity audi…
Spectral Disentanglement and Enhancement: A Dual-domain Contrastive Framework for Representation Learning
Jinjin Guo, Yexin Li, Zhichao Huang +5
Large-scale multimodal contrastive learning has recently achieved impressive success in learning rich and transferable representations, yet it remains fundamentally limited by the…
FANoise: Singular Value-Adaptive Noise Modulation for Robust Multimodal Representation Learning
Jiaoyang Li, Jun Fang, Tianhao Gao +5
Representation learning is fundamental to modern machine learning, powering applications such as text retrieval and multimodal understanding. However, learning robust and generaliz…