Factorial Hidden Restricted Boltzmann Machines for noise robust speech recognition

By Steven J. Rennie, Petr Fousek, Pierre L. Dognin. 2012

8 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. Dong Yu
2. Li Deng
3. Jinyu Li
4. Khe Chai Sim
5. Bo Li
6. Yifan Gong
7. Tara N. Sainath
8. Frank Torsten Bernd Seide

In Signal Processing

1. Dong Yu
2. Li Deng
3. Jinyu Li
4. Khe Chai Sim
5. Bo Li
6. Yifan Gong
7. Frank Torsten Bernd Seide
8. Tara N. Sainath

Citations in the corpus

• Deep Learning: Methods and Applications (2014, methodology): “[300] explored the use of a version of the RBM, called the factorial hidden RBM, for noise-robust speech recognition.”
• A Spectral Masking Approach to Noise-Robust Speech Recognition Using Deep Neural Networks (2014, methodology): “In [8], a Factorial Hidden Restricted Boltzmann Machine (FHRBM) was proposed to explicitly model the noise distribution and how noise affects speech.”