Name
Abstract | ||
|---|---|---|
Despite the widespread use of supervised learning methods for speech emotion recognition, they are severely restricted due to the lack of sufficient amount of labelled speech data for the training. Considering the wide availability of unlabelled speech data, therefore, this paper proposes semisupervised autoencoders to improve speech emotion recognition. The aim is to reap the benefit from the com... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TASLP.2017.2759338 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | Field | DocType |
Speech,Speech recognition,Emotion recognition,Semisupervised learning,Supervised learning,Speech processing,Training | Speech processing,Semi-supervised learning,Autoencoder,Pattern recognition,Computer science,Emotion recognition,Supervised learning,Speech recognition,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 1 | 2329-9290 |
Citations | PageRank | References |
11 | 0.61 | 45 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Deng | 1 | 278 | 18.59 |
| Xinzhou Xu | 2 | 38 | 3.45 |
| Zixing Zhang | 3 | 397 | 31.73 |
| Sascha Frühholz | 4 | 22 | 3.01 |
| Björn Schuller | 5 | 6749 | 463.50 |