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
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
Nithin Rao Koluguri, Samuel Kriman, Georgy Zelenfroind, Somshubra Majumdar, Dima Rekesh, Vahid Noroozi, Jagadeesh Balam, Boris Ginsburg
Improved Factorized Neural Transducer Model For text-only Domain Adaptation
Junzhe Liu, Jianwei Yu, Xie Chen
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining
Lang Cao
Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture
Gaobin Yang, Maokui He, Shutong Niu, Ruoyu Wang, Yanyan Yue, Shuangqing Qian, Shilong Wu, Jun Du, Chin-Hui Lee
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation
Jiaxu Zhu, Weinan Tong, Yaoxun Xu, Changhe Song, Zhiyong Wu, Zhao You, Dan Su, Dong Yu, Helen Meng
SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge
Jiaxu Zhu, Changhe Song, Zhiyong Wu, Helen Meng
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge
Ruoyu Wang, Maokui He, Jun Du, Hengshun Zhou, Shutong Niu, Hang Chen, Yanyan Yue, Gaobin Yang, Shilong Wu, Lei Sun, Yanhui Tu, Haitao Tang, Shuangqing Qian, Tian Gao, Mengzhi Wang, Genshun Wan, Jia Pan, Jianqing Gao, Chin-Hui Lee
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data
Jin Bok Park, Jinkyu Lee, Muhyun Back, Hyunmin Han, David T. Ma, Sang Min Won, Sung Soo Hwang, Il Yong Chun