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pro vyhledávání: '"Liu, ZhiJun"'
This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward; i
Externí odkaz:
http://arxiv.org/abs/2409.09351
Autor:
Li, Chengsheng, Liu, Zhijun
Digital Volume Correlation (DVC) is widely used for the analysis of three-dimensional displacement and strain fields based on CT scans. However, the applicability of DVC methods is limited when it comes to geomaterials: CT speckles are directly corre
Externí odkaz:
http://arxiv.org/abs/2407.11287
Audio language models have recently emerged as a promising approach for various audio generation tasks, relying on audio tokenizers to encode waveforms into sequences of discrete symbols. Audio tokenization often poses a necessary compromise between
Externí odkaz:
http://arxiv.org/abs/2406.05551
Autor:
Huang, Bin, Zhao, Changchen, Liu, Zimeng, Hong, Shenda, Zhang, Baochang, Lu, Hao, Liu, Zhijun, Wang, Wenjin, Liu, Hui
Good health and well-being is among key issues in the United Nations 2030 Sustainable Development Goals. The rising prevalence of large-scale infectious diseases and the accelerated aging of the global population are driving the transformation of hea
Externí odkaz:
http://arxiv.org/abs/2406.07558
Autor:
Du, Chenpeng, Guo, Yiwei, Shen, Feiyu, Liu, Zhijun, Liang, Zheng, Chen, Xie, Wang, Shuai, Zhang, Hui, Yu, Kai
The utilization of discrete speech tokens, divided into semantic tokens and acoustic tokens, has been proven superior to traditional acoustic feature mel-spectrograms in terms of naturalness and robustness for text-to-speech (TTS) synthesis. Recent p
Externí odkaz:
http://arxiv.org/abs/2306.07547
In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then
Externí odkaz:
http://arxiv.org/abs/2304.11750
In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSSs) of a large-dimensional sample covariance matrix when the population covariance matrices are involved with diverging spikes. This constitutes a nontrivia
Externí odkaz:
http://arxiv.org/abs/2212.05896
With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in
Externí odkaz:
http://arxiv.org/abs/2210.04699
In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSS) of large-dimensional sample covariance matrix when the population covariance matrices are not uniformly bounded, which is a nontrivial extension of the B
Externí odkaz:
http://arxiv.org/abs/2205.07280
Autor:
Kong, Yaqiong, Qin, Guoxu, Liu, Zhijun, Cheng, Lehua, Wang, Chunyu, Wu, Fengyi, Wu, Rong, Wang, Qian, Cao, Duojun
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 15 December 2024 323
