Histogram equalization of speech representation for robust speech recognition

By Á. D. L. Torre, A. Peinado, José C. Segura, J. L. Pérez, C. Benítez, Antonio J. Rubio. 2005

357 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Speech & Audio, 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. Li Deng
3. Dong Yu
4. Yifan Gong
5. Dan Hendrycks
6. Yoshua Bengio
7. Jianfeng Gao
8. Thomas G. Dietterich

In Natural Language Processing

1. Jinyu Li
2. Li Deng
3. Yifan Gong
4. Reinhold Häb-Umbach
5. Thomas G. Dietterich
6. Dan Hendrycks
7. Dong Yu
8. Jianfeng Gao

In Speech & Audio

1. Jinyu Li
2. Yifan Gong
3. Li Deng
4. Reinhold Häb-Umbach
5. Thomas G. Dietterich
6. Dan Hendrycks
7. Dong Yu
8. Yoshua Bengio

In Signal Processing

1. Jinyu Li
2. Yifan Gong
3. Dong Yu
4. Li Deng
5. Dan Hendrycks
6. Reinhold Häb-Umbach
7. Thomas G. Dietterich
8. S. Renals

Citations in the corpus

• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019, methodology): “Additional robustness has been achieved through pre-processing techniques such as standardizing the statistics of the input (Liu et al., 1993; Torre et al., 2005; Harvilla & Stern, 2012; Kim & Stern, 2016).”
• An Overview of Noise-Robust Automatic Speech Recognition (2014, background): “While typically achieving higher accuracy than feature-domain methods, they usually incur signi fi cantly larger computational cost.”
• Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations (2018, methodology): “Histogram equalization successfully standardizes speech data for robust speech recognition [54, 18].”