Experimental study in development of speech corpus for emotion recognition with data validation

2015 ◽  
Author(s):  
Ioan Pavaloi ◽  
Elena Musca
Keyword(s):  
Experimental Study ◽  
Emotion Recognition ◽  
Data Validation ◽  
Speech Corpus

scholarly journals Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

2020 ◽  
Vol 10 (5) ◽  
pp. 4752
Author(s):  
Vishal P. Tank ◽  
S. K. Hadia
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Emotional States ◽  
Emotional Speech ◽  
Speech Corpus ◽  
Speech Database ◽  
Machine Communication ◽  
Gujarati Language

In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

scholarly journals Comparison of speaker dependent and speaker independent emotion recognition

2013 ◽  
Vol 23 (4) ◽  
pp. 797-808 ◽  
Author(s):  
Jan Rybka ◽  
Artur Janicki
Keyword(s):  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Speech Corpus ◽  
Training Corpus ◽  
Vector Machines ◽  
Speech Corpora ◽  
Speaker Independent ◽  
Artificial Neural Network Ann

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.

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