Title
Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition
Abstract
This study addresses a situation in practice where training and test samples come from different corpora - here in acoustic emotion recognition. In this situation, a model is trained on one database while tested on another disjoint one. The typical inherent mismatch between the corpora and by that between test and training set usually leads to significant performance degradation. To cope with this problem when no training data from the target domain exists, we propose a `shared-hidden-layer autoencoder' (SHLA) approach for learning common feature representations shared across the training and test set in order to reduce the discrepancy in them. To exemplify effectiveness of our approach, we select the Interspeech Emotion Challenge's FAU Aibo Emotion Corpus as test database and two other publicly available databases as training set for extensive evaluation. The experimental results show that our SHLA method significantly improves over the baseline performance and outperforms today's state-of-the-art domain adaptation methods.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6854517
ICASSP
Keywords
Field
DocType
test database,feature representation,speech processing,shared-hidden-layer autoencoder,inherent mismatch,fau aibo emotion corpus,cross-corpus,transfer learning,performance degradation,target domain,emotion recognition,acoustic signal processing,feature extraction,shla approach,acoustic emotion recognition,signal reconstruction,shared-hidden-layer autoencoders,interspeech emotion challenge,databases,acoustics,speech recognition,speech
Training set,Disjoint sets,Autoencoder,Emotion recognition,Domain adaptation,Computer science,Transfer of learning,Speech recognition,AIBO,Artificial intelligence,Machine learning,Test set
Conference
ISSN
Citations 
PageRank 
1520-6149
20
0.73
References 
Authors
15
5
Name
Order
Citations
PageRank
Jun Deng127818.59
Rui Xia254035.70
Zixing Zhang339731.73
Yang Liu 00044282.56
Björn Schuller56749463.50