An investigation of depressed speech detection: features and normalization

In recent years, the problem of automatic detection of mental illness from the speech signal has gained some initial interest, however questions remaining include how speech segments should be selected, what features provide good discrimination, and what benefits feature normalization might bring given the speaker-specific nature of mental disorders. In this paper, these questions are addressed empirically using classifier configurations employed in emotion recognition from speech, evaluated on a 47-speaker depressed/neutral read sentence speech database. Results demonstrate that (1) detailed spectral features are well suited to the task, (2) speaker normalization provides benefits mainly for less detailed features, and (3) dynamic information appears to provide little benefit. Classification accuracy using a combination of MFCC and formant based features approached 80 % for this database. Index Terms: mental state recognition, depressed speech, feature comparison, MFCC, Gaussian mixture models