  Automatic speech quality assessment is essential for audio researchers,
developers, speech and language pathologists, and system quality engineers. The
current state-of-the-art systems are based on framewise speech features
(hand-engineered or learnable) combined with time dependency modeling. This
paper proposes an efficient system with results comparable to the best
performing model in the ConferencingSpeech 2022 challenge. Our proposed system
is characterized by a smaller number of parameters (40-60x), fewer FLOPS
(100x), lower memory consumption (10-15x), and lower latency (30x). Speech
quality practitioners can therefore iterate much faster, deploy the system on
resource-limited hardware, and, overall, the proposed system contributes to
sustainable machine learning. The paper also concludes that framewise
embeddings outperform utterance-level embeddings and that multi-task training
with acoustic conditions modeling does not degrade speech quality prediction
while providing better interpretation.
