Ses duygu tanima sistemlerinde öznitelik seçme yöntemlerinin karşilaştirilmasi

Translated title of the contribution: Comparison of feature selection methods in voice based emotion recognition systems

Tolga Atalay, Deger Ayata, Yusuf Yaslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The aim of this paper to compare the effect of feature selection methods in emotion recognition from speech and song. Emotion recognition composes of signal processing, feature extraction and classification steps. Nowadays, many studies have focused on common features of speech and song, and have used sub-task classification approach for these systems. In this paper, speech and song data are merged and processed together to focus on the feature selection phase. Autoencoder, Relief-F and Chi-Square selection methods are selected to increase the accuracy of classification. Although selecting features can output similar results, using Relief-F method and Mel Frequency Cepstral Coefficient type of feature outperform these already achieved accuracy rates.

Translated title of the contributionComparison of feature selection methods in voice based emotion recognition systems
Original languageTurkish
Title of host publication26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538615010
DOIs
Publication statusPublished - 5 Jul 2018
Event26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey
Duration: 2 May 20185 May 2018

Publication series

Name26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/TerritoryTurkey
CityIzmir
Period2/05/185/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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