  The estimation of speaker characteristics such as age and height is a
challenging task, having numerous applications in voice forensic analysis. In
this work, we propose a bi-encoder transformer mixture model for speaker age
and height estimation. Considering the wide differences in male and female
voice characteristics such as differences in formant and fundamental
frequencies, we propose the use of two separate transformer encoders for the
extraction of specific voice features in the male and female gender, using
wav2vec 2.0 as a common-level feature extractor. This architecture reduces the
interference effects during backpropagation and improves the generalizability
of the model. We perform our experiments on the TIMIT dataset and significantly
outperform the current state-of-the-art results on age estimation.
Specifically, we achieve root mean squared error (RMSE) of 5.54 years and 6.49
years for male and female age estimation, respectively. Further experiment to
evaluate the relative importance of different phonetic types for our task
demonstrate that vowel sounds are the most distinguishing for age estimation.

Sound;Machine Learning;Audio and Speech Processing