About: Recognizing Emotions from Human Speech Using 2-D Neural Classifier and Influence the Selection of Input Parameters on its Accuracy     Goto   Sponge   NotDistinct   Permalink

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Description
  • This paper deals with the comparison of different methods of speech features extraction for a neural network classifier. We have used a Kohohen self-organizing feature map (SOM) for output-stage classifier which is a specific type of artificial neural nets. The result of this research deals with the accuracy of emotion classifier and compares the two input combinations.
  • This paper deals with the comparison of different methods of speech features extraction for a neural network classifier. We have used a Kohohen self-organizing feature map (SOM) for output-stage classifier which is a specific type of artificial neural nets. The result of this research deals with the accuracy of emotion classifier and compares the two input combinations. (en)
Title
  • Recognizing Emotions from Human Speech Using 2-D Neural Classifier and Influence the Selection of Input Parameters on its Accuracy
  • Recognizing Emotions from Human Speech Using 2-D Neural Classifier and Influence the Selection of Input Parameters on its Accuracy (en)
skos:prefLabel
  • Recognizing Emotions from Human Speech Using 2-D Neural Classifier and Influence the Selection of Input Parameters on its Accuracy
  • Recognizing Emotions from Human Speech Using 2-D Neural Classifier and Influence the Selection of Input Parameters on its Accuracy (en)
skos:notation
  • RIV/61989100:27240/13:86086901!RIV14-MSM-27240___
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  • S
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  • 101753
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  • RIV/61989100:27240/13:86086901
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  • neural network; fundamental frequency; emotions; Digital speech processing (en)
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  • [A276BC814620]
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  • Belgrade
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  • New York
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  • 2013 21st Telecommunications Forum Telfor, TELFOR 2013 - Proceedings of Papers
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  • Vozňák, Miroslav
  • Partila, Pavol
  • Jakovlev, Sergej
  • Mehic, Miralem
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number of pages
http://bibframe.org/vocab/doi
  • 10.1109/TELFOR.2013.6716272
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  • IEEE
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  • 978-1-4799-1419-7
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  • 27240
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