Analyzing features for automatic age estimation on cross-sectional data
Werner Spiegl, Georg Stemmer, Eva Lasarcyk, Varada Kolhatkar, Andrew S. Cassidy, Blaise Potard, Stephen H. Shum, Young Chol Song, Puyang Xu, Peter Beyerlein, James D. Harnsberger, Elmar Nöth · 2009
We develop an acoustic feature set for the estimation of a per-son’s age from a recorded speech signal. The baseline features are Mel-frequency cepstral coefficients (MFCCs) which are ex-tended by various prosodic features, pitch and formant frequen-cies. From experiments on the University of Florida Vocal Ag-ing Database we can draw different conclusions. On the one hand, adding prosodic, pitch and formant features to the MFCC baseline leads to relative reductions of the mean absolute error between 4-20%. Improvements are even larger when percep-tual age labels are taken as a reference. On the other hand, reasonable results with a mean absolute error in age estimation of about 12 years are already achieved using a simple gender-independent setup and MFCCs only. Future experiments will evaluate the robustness of the prosodic features against channel variability on other databases and investigate the differences be-tween perceptual and chronological age labels.