Talker age estimation using machine learning.

As a person ages, the acoustic characteristics of the voice change. Understanding how the sound of a voice changes with age may give insight into physiological changes related to vocal function. Previous work has shown changes in acoustical parameters with chronological age, as well as differences between listener-perceived age and chronological age. However, much of this previous work was done using cross-sectional speech samples, which will show changes with age but may average out important variability with regard to individual aging differences. The current study used a longitudinal recording sample gathered from a corpus of speeches from a single individual spanning about 50 years (48 to 97 years of age). This study investigates how the voice changes with age using both chronological age and perceived age as independent variables; perceived age data were obtained in a previous direct age estimation study. Using the longitudinal recordings, a range of voice and speech acoustic parameters were extracted. These parameters were fitted to a supervised learning model to predict chronological age and perceived age. Differences between the chronological age and perceived age models as well as the usefulness of the various acoustic parameters will be discussed.