Nonlinear Compensation Using the Gauss–Newton Method for Noise-Robust Speech Recognition

By Yong Zhao, Biing-Hwang Fred Juang. 2012

12 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jinyu Li
2. Dong Yu
3. Li Deng
4. Yifan Gong
5. Yoshua Bengio
6. Frank Torsten Bernd Seide
7. Jianfeng Gao
8. Xiaodong He

In Signal Processing

1. Jinyu Li
2. Dong Yu
3. Yifan Gong
4. Li Deng
5. S. Renals
6. M. Seltzer
7. Chin-Hui Lee
8. Frank Torsten Bernd Seide

Citations in the corpus

• Deep Learning: Methods and Applications (2014, background): “See a comprehensive review in [220] and some additional review literature or original work in [4, 82, 119, 140, 230, 370, 404, 431, 444].”
• An Overview of Noise-Robust Automatic Speech Recognition (2014, methodology): “In [193], a Gauss-Newton method is used by discarding the second derivative of the residual with respect to the distortion parameters when calculating the Hessian.”