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- W1519540128 abstract "This paper examines whether anisotropic filtering of spectrograms can enhance a spectrogram based feature extraction method for automatic emotion classification in speech. The emotion classification results are compared between features extracted from non-filtered and filtered spectrograms. We have recently introduced a spectrogram based feature extraction method which calculates the average spectral energy in the equivalent rectangular bands (ERB) of the spectrograms, and then applies the Gaussian mixture model to classify different stress levels or types of emotions. It was examined whether our method can be improved by applying anisotropic filtering of spectrograms to reduce the noise and enhance the contrast of the spectral images. The filtering approach was tested using Speech Under Simulated and Actual Stress (SUSAS) with 3 different stress levels and the Oregon Research Institute (ORI) database with 5 different emotions. In the case of single vowels, a clear improvement of classification results due to anisotropic filtering was observed. For a closed set of single words the improvement was very small, and in the case of long utterances consisting of words from an open set, a clear decrease of the classification results was observed." @default.
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- W1519540128 date "2009-01-01" @default.
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- W1519540128 title "Effect of Anisotropic Filtering of Spectrograms on Emotion Classification in Speech" @default.
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