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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 Classification of Affect in Spoken Utterances Nitin Sawhney In: http://nitin.www.media.mit.edu/people/nitin/affect/affect_svm.ps (1999) Abstract: Prosodic patterns in speech convey affective state to listeners. In infant-directed speech such characteristics are exaggerated, hence such data was utilized to conduct a short study for the classification of affect. Pitch and energy measures extracted from the utterances of 12 speakers provided prosodic features for the classifier. A Support Vector Machine (SVM) approach was used to run a series of machine learning experiments for a 3-way classification of attention, prohibition and approval in these utterances. Due to limited data, cross-validation techniques provided better performance. A linear kernel SVM resulted in an overall accuracy of 65% using a simple set of acoustic features (as previously reported in the literature using other techniques). Introduction The goal of this project is to develop a representation and utilize learning techniques that allows machines to recognize affect in a human speaker's voice. The focus is on early pre-linguistic affective messages in uttera. URL: http://nitin.www.media.mit.edu/people/nitin/affect/affect_svm.ps http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.3042 BASE Hide details

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