Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.
Some pioneer works have systematically evaluated cross-corpora speech emotion recognition with a number of high-quality databases @cite_6 @cite_12 . These works, unfortunately, do not involve any adaptation techniques for reducing the domain shift. @cite_3 first treats the mismatch in emotional data as covariate shift and proposes compensating for that shift by classical importance-weighting at the instance level. At the feature level, some autoencoder-based transfer learning methods @cite_10 @cite_8 have developed to seek a shared feature representation so that the knowledge can be transferred between the domains. All of these methods, however, are usually applied to unsupervised rather than supervised domain adaptation, which demands a lot of data in target domain that may not be easy to collect in reality.
@cite_8 @cite_3 @cite_6 @cite_10 @cite_12
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With the availability of speech data obtained from different devices and varied acquisition conditions, we are often faced with scenarios, where the intrinsic discrepancy between the training and the test data has an adverse impact on affective speech analysis. To address this issue, this letter introduces an Adaptive Denoising Autoencoder based on an unsupervised domain adaptation method, where prior knowledge learned from a target set is used to regularize the training on a source set. Our goal is to achieve a matched feature space representation for the target and source sets while ensuring target domain knowledge transfer. The method has been successfully evaluated on the 2009 INTERSPEECH Emotion Challenge’s FAU Aibo Emotion Corpus as target corpus and two other publicly available speech emotion corpora as sources. The experimental results show that our method significantly improves over the baseline performance and outperforms related feature domain adaptation methods. Pattern recognition tasks often face the situation that training data are not fully representative of test data. This problem is well-recognized in speech recognition, where methods like cepstral mean normalization (CMN), vocal tract length normalization (VTLN) and maximum likelihood linear regression (MLLR) are used to compensate for channel and speaker differences. Speech emotion recognition (SER) is an important emerging field in human-computer interaction and faces the same data shift problems, a fact which has been generally overlooked in this domain. In this paper, we show that compensating for channel and speaker differences can give significant improvements in SER by modelling these differences as a covariate shift. We employ three algorithms from the domain of transfer learning that apply importance weights (IWs) within a support vector machine classifier to reduce the effects of covariate shift. We test these methods on the FAU Aibo Emotion Corpus, which was used in the Interspeech 2009 Emotion Challenge. It consists of two separate parts recorded independently at different schools; hence the two parts exhibit covariate shift. Results show that the IW methods outperform combined CMN and VTLN and significantly improve on the baseline performance of the Challenge. The best of the three methods also improves significantly on the winning contribution to the Challenge. We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder’s hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65 to 78 average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50 while improving classification performance. 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.