End to End
"End-to-end" systems aim to streamline complex processes by integrating multiple stages into a single, unified model, eliminating the need for intermediate steps and potentially improving efficiency and performance. Current research focuses on applying this approach across diverse fields, utilizing architectures like transformers, reinforcement learning, and spiking neural networks to tackle challenges in autonomous driving, robotics, speech processing, and natural language processing. This approach offers significant potential for improving the accuracy, speed, and robustness of various applications, while also simplifying development and deployment.
Papers
SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech
Hyunjae Cho, Wonbin Jung, Junhyeok Lee, Sang Hoon Woo
End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue
Kentaro Mitsui, Tianyu Zhao, Kei Sawada, Yukiya Hono, Yoshihiko Nankaku, Keiichi Tokuda
Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project
Jan Lehečka, Josef V. Psutka, Josef Psutka
Residual Language Model for End-to-end Speech Recognition
Emiru Tsunoo, Yosuke Kashiwagi, Chaitanya Narisetty, Shinji Watanabe
NatiQ: An End-to-end Text-to-Speech System for Arabic
Ahmed Abdelali, Nadir Durrani, Cenk Demiroglu, Fahim Dalvi, Hamdy Mubarak, Kareem Darwish
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen
End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment
Yongli Zhu, Chanan Singh