Name
Abstract | ||
|---|---|---|
The Denoising autoencoder (DAE) has been successfully applied to acoustic emotion recognition lately. In this paper, we adopt the framework of the modified DAE introduced in that projects the input signal to two different hidden representations, for neutral and emotional speech respectively, and uses the emotional representation for the classification task. We propose to model gender information for more robust emotional representation in this work. For neutral representation, male and female dependent DAEs are built using non-emotional speech with the aim of capturing distinct information between the two genders. The emotional hidden representation is shared for the two genders in order to model more emotion specific characteristics, and is used as features in a back-end classifier for emotion recognition. We propose different optimization objectives in training the DAEs. Our experimental results show improvement on unweighted accuracy compared with previous work using the modified DAE method and the classifiers using the standard static features. Further performance gain can be achieved by structural level system combination. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853745 | ICASSP |
Keywords | Field | DocType |
emotional speech representation,signal representation,image representation,emotional classification,image coding,speech recognition,structural level system combination,denoising autoencoder,dae,image denoising,neutral speech representation,emotion recognition,image classification,optimization,acoustic emotion recognition,back-end classifier,gender information modeling,speech coding,emotional hidden representation,gender,feature extraction,speech,noise reduction | System combination,Pattern recognition,Emotion recognition,Computer science,Speech recognition,Artificial intelligence,Denoising autoencoder,Classifier (linguistics) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 8 | 0.48 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Xia | 1 | 540 | 35.70 |
| Jun Deng | 2 | 278 | 18.59 |
| Björn Schuller | 3 | 6749 | 463.50 |
| Yang Liu 0004 | 4 | 28 | 2.56 |