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Abstract

The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.
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Abstract

LABLITA-Suite. Resources for the acquisition of Italian as a second language – LABLITA-suite provides technology-enhanced learning resources for the acquisition of Italian L2. IMAGACT allows for mastering the semantic properties of action verbs in the early phases of language acquisition. The LABLITA corpus of Spoken Italian can be used for training learners for face to face conversations. RIDIRE and CORDIC provide corpus linguistic tools for accessing Italian phraseology, which is useful for enhancing writing capabilities in the various domains of language usage.
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