Title :
Investigating the role of glottal features in classifying clinical depression
Author :
Moore, Elliot, II ; Clements, Mark ; Peifer, John ; Weisser, Lydia
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
Classifying emotion and emotion related disorders in the voice have often been studied utilizing prosodic (pitch, energy, speaking rate) and other spectral characteristics (formants, power spectral density) of the acoustic speech signal. Glottal waveform features have received little attention in the study of many emotion and emotion related disorders, but have shown strong correlations in a variety of speech pattern studies including speaker characterization and stress analysis. We employ glottal extraction techniques to obtain features related to timing, ratios, shimmer, and spectral characteristics of the glottal waveform in the study of clinical depression. Our study reports on several glottal waveform features that show very good separation among a control group and patient group of males and females suffering from a depressive disorder.
Keywords :
acoustic signal processing; emotion recognition; feature extraction; medical signal processing; signal classification; source separation; spectral analysis; speech processing; acoustic speech signal; clinical depression; depressive disorder; emotion; glottal features; prosodic; speaker characterization; spectral characteristics; speech pattern studies; stress analysis; voice; Acoustic waves; Biomedical acoustics; Biomedical engineering; Educational institutions; Loudspeakers; Pattern analysis; Psychiatry; Speech analysis; Speech processing; Stress;
Conference_Titel :
Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE
Print_ISBN :
0-7803-7789-3
DOI :
10.1109/IEMBS.2003.1280512
