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The typical inherent mismatch between the test and training corpora and by that between 'target' and 'source' sets usually leads to significant performance downgrades. To cope with this, this study presents a feature transfer learning method using Denoising Auto encoders (DAEs) to build high order subspaces of the source and target corpora, where features in the source domain are transferred to the...
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...
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