<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Comparison of speaker dependent and speaker independent emotion recognition

Institute of Computer Science Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

Institute of Telecommunications Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Comparison of speaker dependent and speaker independent emotion recognition"}

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...

Comparison of speaker dependent and speaker independent emotion recognition

Publié en ligne: 31 déc. 2013

Pages: 797 - 808

DOI: https://doi.org/10.2478/amcs-2013-0060

This content is open access.