  This paper focuses on room fingerprinting, a task involving the analysis of
an audio recording to determine the specific volume and shape of the room in
which it was captured. While it is relatively straightforward to determine the
basic room parameters from the Room Impulse Responses (RIR), doing so from a
speech signal is a cumbersome task. To address this challenge, we introduce a
dual-encoder architecture that facilitates the estimation of room parameters
directly from speech utterances. During pre-training, one encoder receives the
RIR while the other processes the reverberant speech signal. A contrastive loss
function is employed to embed the speech and the acoustic response jointly. In
the fine-tuning stage, the specific classification task is trained. In the test
phase, only the reverberant utterance is available, and its embedding is used
for the task of room shape classification. The proposed scheme is extensively
evaluated using simulated acoustic environments.
