Semi-supervised ensemble DNN acoustic model training
By Sheng Li, Xugang Lu, Shinsuke Sakai, Masato Mimura, Tatsuya Kawahara. 2017
19 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Optimization & Training, 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
- Abdel-rahman Mohamed
- Alexei Baevski
- Ponnuthurai Nagaratnam Suganthan
- M. A. Ganaie
- Mohammad Tanveer
- Minghui Hu
- Dong Yu
- Michael Auli
In Optimization & Training
- M. A. Ganaie
- Ponnuthurai Nagaratnam Suganthan
- Mohammad Tanveer
- Minghui Hu
- Abdel-rahman Mohamed
- Alexei Baevski
In Speech & Audio
- Abdel-rahman Mohamed
- Alexei Baevski
- Wei-Ning Hsu
- Jinyu Li
- Michael Auli
- Dong Yu
- Yu Zhang
- Alexis Conneau
In Signal Processing
- Abdel-rahman Mohamed
- Alexei Baevski
- M. A. Ganaie
- Ponnuthurai Nagaratnam Suganthan
- Mohammad Tanveer
- Minghui Hu
- Dong Yu
- Li Deng
Citations in the corpus
- Ensemble deep learning: A review (2021, background): “2017 Deep incremental boosting [70] Boosting Classification 2017 Semi-supervised ensemble DNN acoustic model training [149] Semi Supervised Speech Recognition”
- Effectiveness of Self-Supervised Pre-Training for ASR (2020, background): “The practical need for building ASR systems for new conditions with limited resources spurred a lot of work focused on unsupervised speech recognition and representation learning [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], in addition to semi- and weakly-supervised learning techniques to reduce th”