With the rapid development of generation model, AI-based face manipulation technology, which called DeepFakes, has become more and more realistic.
However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data.
no code implementations • 10 Oct 2019 • Manzhang Xu, Bijun Tang, Yuhao Lu, Chao Zhu, Lu Zheng, Jingyu Zhang, Nannan Han, Yuxi Guo, Jun Di, Pin Song, Yongmin He, Lixing Kang, Zhiyong Zhang, Wu Zhao, Cuntai Guan, Xuewen Wang, Zheng Liu
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties.
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN).
We also employ this approach to deal with out-of-language words in the task of multi-lingual speech recognition.
Compared to the conventional layer-wise methods, this new method does not care about the model structure, so can be used to pre-train very complex models.
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification.
Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision.