Name
Title | ||
|---|---|---|
Learning Self-Informed Feature Contribution for Deep Learning-Based Acoustic Modeling. |
Abstract | ||
|---|---|---|
In this paper, we introduce a new feature engineering approach for deep learning-based acoustic modeling, which utilizes input feature contributions. For this purpose, we propose an auxiliary deep neural network (DNN) called a feature contribution network (FCN) whose output layer is composed of sigmoid-based contribution gates. In our framework, the FCN tries to learn element-level discriminative ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TASLP.2018.2858923 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | Field | DocType |
Acoustics,Hidden Markov models,Logic gates,Feature extraction,Speech recognition,Training,Artificial neural networks | Pattern recognition,Computer science,Feature extraction,Multiplication,Feature engineering,Artificial intelligence,Deep learning,Hidden Markov model,Artificial neural network,Discriminative model,Acoustic model | Journal |
Volume | Issue | ISSN |
26 | 11 | 2329-9290 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Younggwan Kim | 1 | 17 | 6.11 |
| Myung Jong Kim | 2 | 31 | 6.30 |
| Jahyun Goo | 3 | 0 | 1.35 |
| Hoirin Kim | 4 | 0 | 1.01 |