کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
557934 874817 2011 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Spoken emotion recognition using hierarchical classifiers
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Spoken emotion recognition using hierarchical classifiers
چکیده انگلیسی

The recognition of the emotional state of speakers is a multi-disciplinary research area that has received great interest over the last years. One of the most important goals is to improve the voice-based human–machine interactions. Several works on this domain use the prosodic features or the spectrum characteristics of speech signal, with neural networks, Gaussian mixtures and other standard classifiers. Usually, there is no acoustic interpretation of types of errors in the results. In this paper, the spectral characteristics of emotional signals are used in order to group emotions based on acoustic rather than psychological considerations. Standard classifiers based on Gaussian Mixture Models, Hidden Markov Models and Multilayer Perceptron are tested. These classifiers have been evaluated with different configurations and input features, in order to design a new hierarchical method for emotion classification. The proposed multiple feature hierarchical method for seven emotions, based on spectral and prosodic information, improves the performance over the standard classifiers and the fixed features.

Research highlights▶Characterization of emotions based on the acoustical and prosodic features. ▶ Emotional groups are founded in an unsupervised way (SOM) and spectral analysis. ▶ New hierarchical schema to classify emotions is supported by acoustic analysis. ▶ Some features/algorithms perform better in particular emotional groups/emotions. ▶ Multiple feature hierarchical classifier increases baselines classification results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 25, Issue 3, July 2011, Pages 556–570
نویسندگان
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