Pregled bibliografske jedinice broj: 819164
Techniques and applications of emotion recognition in speech
Techniques and applications of emotion recognition in speech // Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016 / Biljanović, Petar (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2016. str. 1551-1556 doi:10.1109/MIPRO.2016.7522336 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 819164 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Techniques and applications of emotion recognition
in speech
Autori
Lugović, Sergej ; Dunđer, Ivan ; Horvat, Marko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016
/ Biljanović, Petar - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2016, 1551-1556
ISBN
978-953-233-086-1
Skup
39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016
Mjesto i datum
Opatija, Hrvatska, 30.05.2016. - 03.06.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
emotion recognition ; speech analysis ; machine learning ; acoustic signal processing ; linguistic speech features ; affective computing ; human-computer interaction
Sažetak
Affective computing opens a new area of research in computer science with the aim to improve the way how humans and machines interact. Recognition of human emotions by machines is becoming a significant focus in recent research in different disciplines related to information sciences and Human- Computer Interaction (HCI). In particular, emotion recognition in human speech is important, as it is the primary communication tool of humans. This paper gives a brief overview of the current state of the research in this area with the aim to underline different techniques that are being used for detecting emotional states in vocal expressions. Furthermore, approaches for extracting speech features from speech datasets and machine learning methods with special emphasis on classifiers are analysed. In addition to the mentioned techniques, this paper also gives an outline of the areas where emotion recognition could be utilised such as healthcare, psychology, cognitive sciences and marketing.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Filozofski fakultet, Zagreb,
Tehničko veleučilište u Zagrebu
