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• W2550197462 abstract "Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database." @default.
• W2550197462 created "2016-11-30" @default.
• W2550197462 creator A5034732045 @default.
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• W2550197462 date "2016-01-01" @default.
• W2550197462 modified "2023-09-27" @default.
• W2550197462 title "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition" @default.
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• W2550197462 doi "https://doi.org/10.4018/ijse.2016010105" @default.
• W2550197462 hasPublicationYear "2016" @default.
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