Investigation of unsupervised adaptation of DNN acoustic models with filter bank input

By Takuya Yoshioka, Anton Ragni, Mark John Francis Gales. 2014

46 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. Brian Kingsbury
5. Vaibhava Goel
6. Yoshua Bengio
7. Tara N. Sainath
8. Jianfeng Gao

In Signal Processing

1. Dong Yu
2. Li Deng
3. Jinyu Li
4. Brian Kingsbury
5. Vaibhava Goel
6. Frank Torsten Bernd Seide
7. Tara N. Sainath
8. Yifan Gong

Citations in the corpus

• Deep Learning: Methods and Applications (2014, background): “Several studies were carried out on unsupervised or semi-supervised adaptation of DNN acoustic models with different types of input features with success [223, 405].”
• Data Augmentation for Deep Neural Network Acoustic Modeling (2015, methodology): “It has been previously used in speaker adaptation for DNN and CNN acoustic models with filter bank input features in LVCSR tasks [31][34].”