  The calculation of most objective speech intelligibility assessment metrics
requires clean speech as a reference. Such a requirement may limit the
applicability of these metrics in real-world scenarios. To overcome this
limitation, we propose a deep learning-based non-intrusive speech
intelligibility assessment model, namely STOI-Net. The input and output of
STOI-Net are speech spectral features and predicted STOI scores, respectively.
The model is formed by the combination of a convolutional neural network and
bidirectional long short-term memory (CNN-BLSTM) architecture with a
multiplicative attention mechanism. Experimental results show that the STOI
score estimated by STOI-Net has a good correlation with the actual STOI score
when tested with noisy and enhanced speech utterances. The correlation values
are 0.97 and 0.83, respectively, for the seen test condition (the test speakers
and noise types are involved in the training set) and the unseen test condition
(the test speakers and noise types are not involved in the training set). The
results confirm the capability of STOI-Net to accurately predict the STOI
scores without referring to clean speech.
