  Speech intelligibility can be affected by multiple factors, such as noisy
environments, channel distortions or physiological issues. In this work, we
deal with the problem of automatic prediction of the speech intelligibility
level in this latter case. Starting from our previous work, a non-intrusive
system based on LSTM networks with attention mechanism designed for this task,
we present two main contributions. In the first one, it is proposed the use of
per-frame modulation spectrograms as input features, instead of compact
representations derived from them that discard important temporal information.
In the second one, two different strategies for the combination of per-frame
acoustic log-mel and modulation spectrograms into the LSTM framework are
explored: at decision level or late fusion and at utterance level or
Weighted-Pooling (WP) fusion. The proposed models are evaluated with the
UA-Speech database that contains dysarthric speech with different degrees of
severity. On the one hand, results show that attentional LSTM networks are able
to adequately modeling the modulation spectrograms sequences producing similar
classification rates as in the case of log-mel spectrograms. On the other hand,
both combination strategies, late and WP fusion, outperform the single-feature
systems, suggesting that per-frame log-mel and modulation spectrograms carry
complementary information for the task of speech intelligibility prediction,
than can be effectively exploited by the LSTM-based architectures, being the
system with the WP fusion strategy and Attention-Pooling the one that achieves
best results.
