  Many people are suffering from voice disorders, which can adversely affect
the quality of their lives. In response, some researchers have proposed
algorithms for automatic assessment of these disorders, based on voice signals.
However, these signals can be sensitive to the recording devices. Indeed, the
channel effect is a pervasive problem in machine learning for healthcare. In
this study, we propose a detection system for pathological voice, which is
robust against the channel effect. This system is based on a bidirectional LSTM
network. To increase the performance robustness against channel mismatch, we
integrate domain adversarial training (DAT) to eliminate the differences
between the devices. When we train on data recorded on a high-quality
microphone and evaluate on smartphone data without labels, our robust detection
system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target
sample labels). To the best of our knowledge, this is the first study applying
unsupervised domain adaptation to pathological voice detection. Notably, our
system does not need target device sample labels, which allows for
generalization to many new devices.
