  The real-world capabilities of objective speech quality measures are limited
since current measures (1) are developed from simulated data that does not
adequately model real environments; or they (2) predict objective scores that
are not always strongly correlated with subjective ratings. Additionally, a
large dataset of real-world signals with listener quality ratings does not
currently exist, which would help facilitate real-world assessment. In this
paper, we collect and predict the perceptual quality of real-world speech
signals that are evaluated by human listeners. We first collect a large quality
rating dataset by conducting crowdsourced listening studies on two real-world
corpora. We further develop a novel approach that predicts human quality
ratings using a pyramid bidirectional long short term memory (pBLSTM) network
with an attention mechanism. The results show that the proposed model achieves
statistically lower estimation errors than prior assessment approaches, where
the predicted scores strongly correlate with human judgments.
