Comparison of Speaker Dependent And Speaker Independent Emotion Recognition
Jan Rybka , Artur Janicki
AbstractThis 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.
|Journal series||International Journal of Applied Mathematics & Computer Science, [2083-8492], ISSN 1641-876X|
|Keywords in Polish||przetwarzanie mowy, rozpoznawanie emocji, EMO-DB, sztuczne sieci neuronowe|
|Keywords in English||speech processing, emotion recognition, EMO-DB, support vector machines, artificial neural networks|
|ASJC Classification||; ;|
|Project||The Develpment of Digital Communicatios. Project leader: Lubacz Józef,
, Phone: 22 234 65 31, start date 04-05-2012, planned end date 31-03-2013, end date 31-12-2013, IT/2012/statut, Completed
|Score|| = 25.0, 02-02-2020, ArticleFromJournal|
= 25.0, 02-02-2020, ArticleFromJournal
|Publication indicators||= 10; = 14; = 19.0; : 2013 = 1.588; : 2013 = 1.39 (2) - 2013=1.317 (5)|
|Citation count*||21 (2020-09-02)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.