10 papers
UAT: Unified Audio-Text Diffusion for Audio Generation, Editing, and Captioning
Hui Wang, Yifan Yang, Zeyue Tian +8
Audio generation and audio-to-text understanding remain largely separate, with diffusion models dominating high-fidelity synthesis and autoregressive (AR) language models driving c…
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training
Yifan Yang, Bing Han, Hui Wang +8
Modeling fine-grained speaking styles remains challenging for language-speech representation pre-training, as existing speech-text models are typically trained with coarse captions…
Measuring Prosody Diversity in Zero-Shot TTS: A New Metric, Benchmark, and Exploration
Yifan Yang, Bing Han, Hui Wang +5
Prosody diversity is essential for achieving naturalness and expressiveness in zero-shot text-to-speech (TTS). However, frequently used acoustic metrics capture only partial views…
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…
AUV: Teaching Audio Universal Vector Quantization with Single Nested Codebook
Yushen Chen, Kai Hu, Long Zhou +4
We propose AUV, a unified neural audio codec with a single codebook, which enables a favourable reconstruction of speech and further extends to general audio, including vocal, musi…
Autoregressive Speech Synthesis without Vector Quantization
Lingwei Meng, Long Zhou, Shujie Liu +9
We present MELLE, a novel continuous-valued token based language modeling approach for text-to-speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram f…