  Most automatic speech processing systems register degraded performance when
applied to noisy or reverberant speech. But how can one tell whether speech is
noisy or reverberant? We propose Brouhaha, a neural network jointly trained to
extract speech/non-speech segments, speech-to-noise ratios, and C50room
acoustics from single-channel recordings. Brouhaha is trained using a
data-driven approach in which noisy and reverberant audio segments are
synthesized. We first evaluate its performance and demonstrate that the
proposed multi-task regime is beneficial. We then present two scenarios
illustrating how Brouhaha can be used on naturally noisy and reverberant data:
1) to investigate the errors made by a speaker diarization model
(pyannote.audio); and 2) to assess the reliability of an automatic speech
recognition model (Whisper from OpenAI). Both our pipeline and a pretrained
model are open source and shared with the speech community.
