  Automatic height and age estimation of speakers using acoustic features is
widely used for the purpose of human-computer interaction, forensics, etc. In
this work, we propose a novel approach of using attention mechanism to build an
end-to-end architecture for height and age estimation. The attention mechanism
is combined with Long Short-Term Memory(LSTM) encoder which is able to capture
long-term dependencies in the input acoustic features. We modify the
conventionally used Attention -- which calculates context vectors the sum of
attention only across timeframes -- by introducing a modified context vector
which takes into account total attention across encoder units as well, giving
us a new cross-attention mechanism. Apart from this, we also investigate a
multi-task learning approach for jointly estimating speaker height and age. We
train and test our model on the TIMIT corpus. Our model outperforms several
approaches in the literature. We achieve a root mean square error (RMSE) of
6.92cm and6.34cm for male and female heights respectively and RMSE of 7.85years
and 8.75years for male and females ages respectively. By tracking the attention
weights allocated to different phones, we find that Vowel phones are most
important whistlestop phones are least important for the estimation task.
