GMM and neural networks were trained for age interval and sex identifications using speech short term features[1]. Homayounpour and Khosravi make an effort on identifying speaker age interval using SVM model and MFCC and LPCC

164 Abstract— Differences of physiological properties of the glottis and the vocal track are partly due to age and/or gender differences. Since these differences are reflected in the speech signal, acoustic measures related to those properties can be helpful for automatic gender classification. Acoustics measures of voice sources were extracted from 10 utterances spoken by 20 male and 20 female talkers (aged 19 to 25 year old). The difference of speech long term features, including zero crossing rate, short time energy, and spectrum flux between male and female is studied. The result shows that the estimation of short time energy reflects more effectively, the difference in male and female voice than zero crossing rate and spectrum flux.