Kohonen network as a classifier of Polish emotional speech
Materiały konferencyjne
MNiSW
15
WOS
| Status: | |
| Autorzy: | Powroźnik Paweł |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2017 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 15 |
| Numer artykułu: | 2004 |
| Strony: | 1 - 6 |
| Web of Science® Times Cited: | 2 |
| Bazy: | Web of Science | Google Scholar |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 2nd International Conference of Computational Methods in Engineering Science |
| Skrócona nazwa konferencji: | CMES’17 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 23 listopada 2017 do 25 listopada 2017 |
| Miasto konferencji: | Lublin |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 15 grudnia 2017 |
| Abstrakty: | angielski |
| The power of speech is a main tool in human communication. There are a lot of factors as age, emotions, gender, pitch of the voice which can influence features of speech. Obviously, information conveyed by voice intonation has more than only textual meaning. The same sentence pronounced in two different ways can have two completely different meanings. This paper describes Kohonen networks as a classifier of Polish emotional speech. The usage of Discrete Wavelet Transform (DWT) as well as an innovative approach to scaleogram processing is also presented in this article. Mexican Hat Wavelet and the Haar Wavelet were used in researches. All simulations were carried out in MatLab 2016 with Neural Network Toolbar. During whole research more than 9000 simulation have been done. Three different speech databases were used in conducted researches. One of them was prepared by professional actors – four women and four men, and contains 240 wav files. Two others are results of researchers works. The structures of used Kohonen networks depend on speech signal decomposition’s level and scaleogram division. During conducted researches the following emotional states were considered: anger, joy, sadness, boredom, fear and neutral state. Achieved results were between 68% and 80% depends of used wavelet, speech signal and signal decomposition’s level |
