3 citations · 3 across the 6 of their papers we have counts for
7 papers
SemanticVocoder: Bridging Audio Generation and Audio Understanding via Semantic Latents
Zeyu Xie, Chenxing Li, Qiao Jin +6
Recent audio generation models typically rely on Variational Autoencoders (VAEs) and perform generation within the VAE latent space. Although VAEs excel at compression and reconstr…
U-Codec: Ultra Low Frame-rate Neural Speech Codec for Fast High-fidelity Speech Generation
Xusheng Yang, Long Zhou, Wenfu Wang +6
We propose \textbf{U-Codec}, an \textbf{U}ltra low frame-rate neural speech \textbf{Codec} that achieves high-fidelity reconstruction and fast speech generation at an extremely low…
PhoenixCodec: Taming Neural Speech Coding for Extreme Low-Resource Scenarios
Zixiang Wan, Haoran Zhao, Guochang Zhang +3
This paper presents PhoenixCodec, a comprehensive neural speech coding and decoding framework designed for extremely low-resource conditions. The proposed system integrates an opti…
When Audio Generators Become Good Listeners: Generative Features for Understanding Tasks
Zeyu Xie, Chenxing Li, Xuenan Xu +6
This work pioneers the utilization of generative features in enhancing audio understanding. Unlike conventional discriminative features that directly optimize posterior and thus em…
FakeSound2: A Benchmark for Explainable and Generalizable Deepfake Sound Detection
Zeyu Xie, Yaoyun Zhang, Xuenan Xu +4
The rapid development of generative audio raises ethical and security concerns stemming from forged data, making deepfake sound detection an important safeguard against the malicio…
STAR: Speech-to-Audio Generation via Representation Learning
Zeyu Xie, Xuenan Xu, Yixuan Li +2
This work presents STAR, the first end-to-end speech-to-audio generation framework, designed to enhance efficiency and address error propagation inherent in cascaded systems. Unlik…
On the Worst Prompt Performance of Large Language Models
Bowen Cao, Deng Cai, Zhisong Zhang +2
The performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts, which raises significant concerns about their reliability in real-world scenarios.…