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.

This letter introduces a novel approach to rotation and scale invariant texture classification. The proposed approach is based on Gabor filters that have the capability to collapse the filter responses according to the scale and orientation of the textures. These characteristics are exploited to first calculate the homogeneous texture of images followed by the rearrangement of features as a two-dimensional matrix (scale and orientation), where scaling and rotation of images correspond to shifting in this matrix. The shift invariance property of discrete fourier transform is used to propose rotation and scale invariant image features. The performance of the proposed feature set is evaluated on Brodatz texture album. Experimental results demonstrate the superiority of the proposed descriptor as compared to other methods considered in this letter.