  Objective: Currently, only behavioral speech understanding tests are
available, which require active participation of the person being tested. As
this is infeasible for certain populations, an objective measure of speech
intelligibility is required. Recently, brain imaging data has been used to
establish a relationship between stimulus and brain response. Linear models
have been successfully linked to speech intelligibility but require per-subject
training. We present a deep-learning-based model incorporating dilated
convolutions that operates in a match/mismatch paradigm. The accuracy of the
model's match/mismatch predictions can be used as a proxy for speech
intelligibility without subject-specific (re)training. Approach: We evaluated
the performance of the model as a function of input segment length, EEG
frequency band and receptive field size while comparing it to multiple baseline
models. Next, we evaluated performance on held-out data and finetuning.
Finally, we established a link between the accuracy of our model and the
state-of-the-art behavioral MATRIX test. Main results: The dilated
convolutional model significantly outperformed the baseline models for every
input segment length, for all EEG frequency bands except the delta and theta
band, and receptive field sizes between 250 and 500 ms. Additionally,
finetuning significantly increased the accuracy on a held-out dataset. Finally,
a significant correlation (r=0.59, p=0.0154) was found between the speech
reception threshold estimated using the behavioral MATRIX test and our
objective method. Significance: Our method is the first to predict the speech
reception threshold from EEG for unseen subjects, contributing to objective
measures of speech intelligibility.

Audio and Speech Processing;Machine Learning;Sound;Signal Processing;Quantitative Methods