  Recently, deep learning (DL)-based non-intrusive speech assessment models
have attracted great attention. Many studies report that these DL-based models
yield satisfactory assessment performance and good flexibility, but their
performance in unseen environments remains a challenge. Furthermore, compared
to quality scores, fewer studies elaborate deep learning models to estimate
intelligibility scores. This study proposes a multi-task speech intelligibility
prediction model, called MTI-Net, for simultaneously predicting human and
machine intelligibility measures. Specifically, given a speech utterance,
MTI-Net is designed to predict human subjective listening test results and word
error rate (WER) scores. We also investigate several methods that can improve
the prediction performance of MTI-Net. First, we compare different features
(including low-level features and embeddings from self-supervised learning
(SSL) models) and prediction targets of MTI-Net. Second, we explore the effect
of transfer learning and multi-tasking learning on training MTI-Net. Finally,
we examine the potential advantages of fine-tuning SSL embeddings. Experimental
results demonstrate the effectiveness of using cross-domain features,
multi-task learning, and fine-tuning SSL embeddings. Furthermore, it is
confirmed that the intelligibility and WER scores predicted by MTI-Net are
highly correlated with the ground-truth scores.
