  Speaker profiling, which aims to estimate speaker characteristics such as age
and height, has a wide range of applications inforensics, recommendation
systems, etc. In this work, we propose a semisupervised learning approach to
mitigate the issue of low training data for speaker profiling. This is done by
utilizing external corpus with speaker information to train a better
representation which can help to improve the speaker profiling systems.
Specifically, besides the standard supervised learning path, the proposed
framework has two more paths: (1) an unsupervised speaker representation
learning path that helps to capture the speaker information; (2) a consistency
training path that helps to improve the robustness of the system by enforcing
it to produce similar predictions for utterances of the same speaker.The
proposed approach is evaluated on the TIMIT and NISP datasets for age, height,
and gender estimation, while the Librispeech is used as the unsupervised
external corpus. Trained both on single-task and multi-task settings, our
approach was able to achieve state-of-the-art results on age estimation on the
TIMIT Test dataset with Root Mean Square Error(RMSE) of6.8 and 7.4 years and
Mean Absolute Error(MAE) of 4.8 and5.0 years for male and female speakers
respectively.
