ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Analyzing features for automatic age estimation on cross-sectional data

Werner Spiegl, Georg Stemmer, Eva Lasarcyk, Varada Kolhatkar, Andrew Cassidy, Blaise Potard, Stephen Shum, Young Chol Song, Puyang Xu, Peter Beyerlein, James Harnsberger, Elmar Nöth

We develop an acoustic feature set for the estimation of a person’s age from a recorded speech signal. The baseline features are Mel-frequency cepstral coefficients (MFCCs) which are extended by various prosodic features, pitch and formant frequencies. From experiments on the University of Florida Vocal Aging 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 perceptual 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 between perceptual and chronological age labels.

doi: 10.21437/Interspeech.2009-740

Cite as: Spiegl, W., Stemmer, G., Lasarcyk, E., Kolhatkar, V., Cassidy, A., Potard, B., Shum, S., Song, Y.C., Xu, P., Beyerlein, P., Harnsberger, J., Nöth, E. (2009) Analyzing features for automatic age estimation on cross-sectional data. Proc. Interspeech 2009, 2923-2926, doi: 10.21437/Interspeech.2009-740

  author={Werner Spiegl and Georg Stemmer and Eva Lasarcyk and Varada Kolhatkar and Andrew Cassidy and Blaise Potard and Stephen Shum and Young Chol Song and Puyang Xu and Peter Beyerlein and James Harnsberger and Elmar Nöth},
  title={{Analyzing features for automatic age estimation on cross-sectional data}},
  booktitle={Proc. Interspeech 2009},