  Speech intelligibility can be degraded due to multiple factors, such as noisy
environments, technical difficulties or biological conditions. This work is
focused on the development of an automatic non-intrusive system for predicting
the speech intelligibility level in this latter case. The main contribution of
our research on this topic is the use of Long Short-Term Memory (LSTM) networks
with log-mel spectrograms as input features for this purpose. In addition, this
LSTM-based system is further enhanced by the incorporation of a simple
attention mechanism that is able to determine the more relevant frames to this
task. The proposed models are evaluated with the UA-Speech database that
contains dysarthric speech with different degrees of severity. Results show
that the attention LSTM architecture outperforms both, a reference Support
Vector Machine (SVM)-based system with hand-crafted features and a LSTM-based
system with Mean-Pooling.
