Publication Type:

Journal Article

Source:

International Journal of Speech Technology, Springer New York LLC, p.1-12 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017883558&doi=10.1007%2fs10772-017-9407-3&partnerID=40&md5=0d8d0959a7ca1947f312060bbac2775e

Keywords:

Audio recordings, Database systems, Electroglottogram, Excitation parameters, Gaussian distribution, Instantaneous pitch (F0), MFCC, Source parameters, Speech, Speech recognition, Strength of excitation (SoE)

Abstract:

<p>The work presented in this paper is focused on the development of a simulated emotion database particularly for the excitation source analysis. The presence of simultaneous electroglottogram (EGG) recordings for each emotion utterance helps to accurately analyze the variations in the source parameters according to different emotions. The work presented in this paper describes the development of comparatively large simulated emotion database for three emotions (Anger, Happy and Sad) along with neutrally spoken utterances in three languages (Tamil, Malayalam and Indian English). Emotion utterances in each language are recorded from 10 speakers in multiple sessions (Tamil and Malayalam). Unlike the existing simulated emotion databases, instead of emotionally neutral utterances, emotionally biased utterances are used for recording. Based on the emotion recognition experiments, the emotions elicited from emotionally biased utterances are found to show more emotion discrimination as compared to emotionally neutral utterances. Also, based on the comparative experimental analysis, the speech and EGG utterances of the proposed simulated emotion database are found to preserve the general trend in the excitation source characteristics (instantaneous F0 and strength of excitation parameters) for different emotions as that of the classical German emotion speech-EGG database (EmoDb). Finally, the emotion recognition rates obtained for the proposed speech-EGG emotion database using the conventional mel frequency cepstral coefficients and Gaussian mixture model based emotion recognition system, are found to be comparable with that of the existing German (EmoDb) and IITKGP-SESC Telugu speech emotion databases. © 2017 Springer Science+Business Media New York</p>

Notes:

cited By 0; Article in Press

Cite this Research Publication

D. Pravena and Dr. Govind D., “Development of simulated emotion speech database for excitation source analysis”, International Journal of Speech Technology, pp. 1-12, 2017.

207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS