Autor:
Współtwórca:
Korbicz, Józef - red. ; Uciński, Dariusz - red.
Tytuł:
Comparison of speaker dependent and speaker independent emotion recognition
Tytuł publikacji grupowej:
Temat i słowa kluczowe:
peech processing ; emotion recognition ; EMO-DB ; support vector machines ; artificial neural networks
Abstract:
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.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Data wydania:
Typ zasobu:
DOI:
Strony:
Źródło:
AMCS, volume 23, number 4 (2013) ; kliknij tutaj, żeby przejść