Title :
Emotion recognition in Romanian language using LPC features
Author :
Feraru, Silvia Monica ; Zbancioc, Marius Dan
Author_Institution :
Inst. of Comput. Sci., Iaşi, Romania
Abstract :
In this study we compare the recognition accuracy of the emotions when in the feature vectors are introduced the LPC coefficients, with previous results obtained with prosodic features (F0-fundamental frequency, F1-F4-formants), respectively MFCC (Mel frequency) cepstral coefficients. From the extended sets of parameters that we introduced LPCC ((linear prediction cepstral coefficients)+LPC+PARCOR (partial correlation coefficient)+LAR (log area ratio) coefficients +AC (autocorrelation coefficients), best results was obtained for PARCOR coefficients when the emotion recognition rate was around 81%.
Keywords :
Gaussian processes; emotion recognition; natural language processing; support vector machines; AC; GMM; LAR; LDA; LPC features; MFCC; Mel frequency cepstral coefficients; PARCOR; Romanian language; SROL database; SVM; autocorrelation coefficients; emotion recognition; feature vectors; linear prediction cepstral coefficients; log area ratio coefficients; partial correlation coefficient; Accuracy; Databases; Emotion recognition; Mel frequency cepstral coefficient; Speech; Speech recognition; Vectors; LPCC; PARCOR; Weighted-KNN; emotion recognition;
Conference_Titel :
E-Health and Bioengineering Conference (EHB), 2013
Conference_Location :
Iasi
Print_ISBN :
978-1-4799-2372-4
DOI :
10.1109/EHB.2013.6707314
