  Traditionally, the quality of acoustic echo cancellers is evaluated using
intrusive speech quality assessment measures such as ERLE \cite{g168} and PESQ
\cite{p862}, or by carrying out subjective laboratory tests. Unfortunately, the
former are not well correlated with human subjective measures, while the latter
are time and resource consuming to carry out. We provide a new tool for speech
quality assessment for echo impairment which can be used to evaluate the
performance of acoustic echo cancellers. More precisely, we develop a neural
network model to evaluate call quality degradations in two separate categories:
echo and degradations from other sources. We show that our model is accurate as
measured by correlation with human subjective quality ratings. Our tool can be
used effectively to stack rank echo cancellation models. AECMOS is being made
publicly available as an Azure service.
