Histogram-based subband powerwarping and spectral averaging for robust speech recognition under matched and multistyle training

By Mark Harvilla, Richard M. Stern. 2012

11 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. Dan Hendrycks
2. Thomas G. Dietterich
3. Balaji Lakshminarayanan
4. Dawn Song
5. Nicholas Carlini
6. Yixuan Li
7. Matthias Hein
8. Igor Mordatch

In Natural Language Processing

1. Thomas G. Dietterich
2. Dan Hendrycks

In Speech & Audio

1. Thomas G. Dietterich
2. Dan Hendrycks

In Signal Processing

1. Dan Hendrycks
2. Thomas G. Dietterich
3. Yilun Du
4. Igor Mordatch
5. Matthias Hein
6. Balaji Lakshminarayanan
7. Yixuan Li
8. Yedid Hoshen

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).”
• Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations (2018, methodology): “Histogram equalization successfully standardizes speech data for robust speech recognition [54, 18].”