Low-dimensional feature space derivation for emotion recognition
نویسندگان
چکیده
An objective of the paper was to determine a set of lowdimensional feature spaces that provide high emotion recognition rates. Candidates for target feature spaces were randomly drawn from a broad pool of speech signal parameters that comprised both commonly used characteristics and newly introduced features. As a result, several four-dimensional feature spaces that provide the highest emotion classification rates (68 %) on Polish language database, which we used in experiments, were identified.
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تاریخ انتشار 2005