Speech Emotion Recognition Using Voiced Segment Selection Algorithm

Yu Gu, Eric Postma, Hai Xiang Lin, Jaap van den Herik

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
48 Downloads (Pure)


Speech emotion recognition (SER) poses one of the major challenges in human-machine interaction. We propose a new algorithm, the Voiced Segment Selection (VSS) algorithm, which can produce an accurate segmentation of speech signals. The VSS algorithm deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification. The finding shows that the VSS method is a more accurate algorithm for voiced segment detection. Therefore, it has potential to improve performance of emotion recognition from speech.
Original languageEnglish
Title of host publicationECAI 2016 - 22nd European Conference on Artificial Intelligence
EditorsGal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen
PublisherIOS Press
Number of pages2
ISBN (Electronic)978-1-61499-672-9
ISBN (Print)978-1-61499-671-2
Publication statusPublished - 2016
EventECAI 2016: 22nd European Conference on Artificial Intelligence 2016 - World Forum, The Hague, Netherlands
Duration: 29 Aug 20162 Sept 2016
Conference number: 22

Publication series

NameFrontiers in Artificial Intelligence and Applications


ConferenceECAI 2016
Abbreviated titleECAI 2016
CityThe Hague
OtherIncluding Prestigious Applications of Artificial Intelligence, PAIS 2016
Internet address


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