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Pregled bibliografske jedinice broj: 1002968

Emotion Classification Based on Convolutional Neural Network Using Speech Data


Vrebčević, Nikola; Mijić, Igor; Petrinović, Davor
Emotion Classification Based on Convolutional Neural Network Using Speech Data // MIPRO 2019 42nd International Convention May 20 – 24, 2019 Opatija, Croatia Proceedings / Skala, Karolj (ur.).
Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2019. str. 1191-1196 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Emotion Classification Based on Convolutional Neural Network Using Speech Data

Autori
Vrebčević, Nikola ; Mijić, Igor ; Petrinović, Davor

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
MIPRO 2019 42nd International Convention May 20 – 24, 2019 Opatija, Croatia Proceedings / Skala, Karolj - Rijeka : Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2019, 1191-1196

Skup
MIPRO 2019 42nd International Convention

Mjesto i datum
Rijeka, Hrvatska, 20-24.05.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
emotions ; speech ; emotion classification ; convolutional neural network ; deep learning

Sažetak
The human voice is the most frequently used mode of communication among people. It carries both linguistic and paralinguistic information. For an emotion classification task, it is important to process paralinguistic information because it describes the current affective state of a speaker. This affective information can be used for health care purposes, customer service enhancement and in the entertainment industry. Previous research in the field mostly relied on handcrafted features that are derived from speech signals and thus used for the construction of mainly statistical models. Today, by using new technologies, it is possible to design models that can both extract features and perform classification. This preliminary research explores the performance of a model that comprises a convolutional neural network for feature extraction and a deep neural network that performs emotion classification. The convolutional neural network consists of three convolutional layers that filter input spectrograms in time and frequency dimensions and two dense layers forming the deep part of the model. The unified neural network is trained and tested spectrograms of speech utterances from the Berlin database of emotional speech.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)



POVEZANOST RADA


Projekti:
0036054
KK.01.1.1.01.009.
DOK-2018-01-2976
HRZZ-IP-2014-09-2625 - Iznad Nyquistove granice (BeyondLimit) (Seršić, Damir, HRZZ - 2014-09) ( POIROT)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Igor Mijić (autor)

Avatar Url Davor Petrinović (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada

Citiraj ovu publikaciju:

Vrebčević, Nikola; Mijić, Igor; Petrinović, Davor
Emotion Classification Based on Convolutional Neural Network Using Speech Data // MIPRO 2019 42nd International Convention May 20 – 24, 2019 Opatija, Croatia Proceedings / Skala, Karolj (ur.).
Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2019. str. 1191-1196 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Vrebčević, N., Mijić, I. & Petrinović, D. (2019) Emotion Classification Based on Convolutional Neural Network Using Speech Data. U: Skala, K. (ur.)MIPRO 2019 42nd International Convention May 20 – 24, 2019 Opatija, Croatia Proceedings.
@article{article, editor = {Skala, K.}, year = {2019}, pages = {1191-1196}, keywords = {emotions, speech, emotion classification, convolutional neural network, deep learning}, title = {Emotion Classification Based on Convolutional Neural Network Using Speech Data}, keyword = {emotions, speech, emotion classification, convolutional neural network, deep learning}, publisher = {Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO}, publisherplace = {Rijeka, Hrvatska} }
@article{article, editor = {Skala, K.}, year = {2019}, pages = {1191-1196}, keywords = {emotions, speech, emotion classification, convolutional neural network, deep learning}, title = {Emotion Classification Based on Convolutional Neural Network Using Speech Data}, keyword = {emotions, speech, emotion classification, convolutional neural network, deep learning}, publisher = {Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO}, publisherplace = {Rijeka, Hrvatska} }




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