Temat: EMO-DB - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Speech emotion recognition system for social robots
Autorzy:: Juszkiewicz, Ł.
Powiązania:: https://bibliotekanauki.pl/articles/384511.pdf
Data publikacji:: 2013
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: speech emotion recognition
prosody
machine learning
Emo-DB
intonation
social robot
Opis:: The paper presents a speech emotion recognition system for social robots. Emotions are recognised using global acoustic features of the speech. The system implements the speech parameters calculation, features extraction, features selection and classification. All these phases are described. The system was verified using the two emotional speech databases: Polish and German. Perspectives for using such system in the social robots are presented.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2013, 7, 4; 59-65
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Comparison of speaker dependent and speaker independent emotion recognition
Autorzy:: Rybka, J.
Janicki, A.
Powiązania:: https://bibliotekanauki.pl/articles/330055.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: speech processing
emotion recognition
EMO-DB
support vector machines
artificial neural network
przetwarzanie mowy
rozpoznawanie emocji
maszyna wektorów wspierających
sztuczna sieć neuronowa
Opis:: This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector Machines (SVMs), were used in experiments. SVMs turned out to provide the best classification accuracy of 75.44% in the speaker dependent mode, that is, when speech samples from the same speaker were included in the training corpus. Various speaker dependent and speaker independent configurations were analyzed and compared. Emotion recognition in speaker dependent conditions usually yielded higher accuracy results than a similar but speaker independent configuration. The improvement was especially well observed if the base recognition ratio of a given speaker was low. Happiness and anger, as well as boredom and neutrality, proved to be the pairs of emotions most often confused.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 797-808
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "EMO-DB" wg kryterium: Temat

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język