Support vector machines for noise robust ASR
By Mark John Francis Gales, Anton Ragni, H. AlDamarki, C. Gautier. 2009
33 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Safety, Robustness & Interpretability, 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
- Jinyu Li
- Dong Yu
- Yoshua Bengio
- Sanjeev Khudanpur
- Daniel Povey
- Shinji Watanabe
- Luke S. Zettlemoyer
- Quoc V. Le
In Safety, Robustness & Interpretability
- Adam Coates
- Awni Y. Hannun
- Mark John Francis Gales
- Bryan Catanzaro
- Dario Amodei
- Andrew Y. Ng
- Mike Chrzanowski
- Sharan Narang
In Speech & Audio
- Sanjeev Khudanpur
- Daniel Povey
- Vijayaditya Peddinti
- Jinyu Li
- Shinji Watanabe
- Tom Ko
- Yu Zhang
- Wei-Ning Hsu
In Signal Processing
- Sanjeev Khudanpur
- Daniel Povey
- Jinyu Li
- Vijayaditya Peddinti
- Tom Ko
- Dong Yu
- Shinji Watanabe
- Awni Y. Hannun
Citations in the corpus
- Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015, background): “This has also been shown to improve speech systems [21, 26].”
- Data augmentation for low resource languages (2014, methodology): “These include standard statistical approaches, such as maximum a posteriori (MAP) estimation [6], and data augmentation [7, 8, 9, 10, 11, 12].”
- Audio augmentation for speech recognition (2015, background): “In [1, 2], corrupting clean training speech with noise was found to improve the robustness of the speech recognizer against noisy speech.”