Robustness against the channel effect in pathological voice detection
This addresses the pervasive channel effect in healthcare machine learning for people with voice disorders, offering a robust detection method that generalizes to new devices without needing labels, though it is incremental as it applies existing domain adaptation techniques to a new domain.
The study tackled the problem of channel effect in pathological voice detection by proposing a system based on a bidirectional LSTM with domain adversarial training, which increased PR-AUC from 0.8448 to 0.9455 when trained on high-quality microphone data and evaluated on smartphone data without labels.
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.