Name
Abstract | ||
|---|---|---|
In previous systems of speech emotion recognition, supervised learning are frequently employed to train classifiers on lots of labeled examples. However, the labeling of abundant data requires much time and many human efforts. This paper presents an enhanced co-training algorithm to utilize a large amount of unlabeled speech utterances for building a semi-supervised learning system. It uses two conditionally independent attribute views(i.e. temporal features and statistic features) of unlabeled examples to augment a much smaller set of labeled examples. Our experimental results demonstrate that compared with the method based on the supervised training, the proposed system makes 9.0% absolute improvement on female model and 7.4% on male model in terms of average accuracy. Moreover, the enhanced co-training algorithm achieves comparable performance to the co-training prototype, while it can reduce the classification noise which is produced by error labeling in the process of semi-supervised learning. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICME.2007.4284821 | 2007 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-5 |
Keywords | Field | DocType |
null | Semi-supervised learning,Computer science,Co-training,Unsupervised learning,Artificial intelligence,Pattern recognition,Conditional independence,Support vector machine,Algorithm,Feature extraction,Speech recognition,Supervised learning,Hidden Markov model | Conference |
Volume | Issue | Citations |
null | null | 16 |
PageRank | References | Authors |
0.93 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liu Jia | 1 | 19 | 1.42 |
| Chun Chen | 2 | 4727 | 246.28 |
| Jiajun Bu | 3 | 4106 | 211.52 |
| Mingyu You | 4 | 160 | 16.22 |
| Jianhua Tao | 5 | 848 | 138.00 |