(2) How to clone a person's voice while controlling the style and prosody.
We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER.
In this paper, we propose a machine learning framework to obtain speech emotion representations by limiting the effect of speaker variability in the speech signals.
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags.
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
In this paper, we propose a method to compress deep neural networks by using the Fisher Information metric, which we estimate through a stochastic optimization method that keeps track of second-order information in the network.