  We present a method for blind acoustic parameter estimation from
single-channel reverberant speech. The method is structured into three stages.
In the first stage, a variational auto-encoder is trained to extract latent
representations of acoustic impulse responses represented as mel-spectrograms.
In the second stage, a separate speech encoder is trained to estimate
low-dimensional representations from short segments of reverberant speech.
Finally, the pre-trained speech encoder is combined with a small regression
model and evaluated on two parameter regression tasks. Experimentally, the
proposed method is shown to outperform a fully end-to-end trained baseline
model.
