تقرير
Robustness against the channel effect in pathological voice detection
| العنوان: | Robustness against the channel effect in pathological voice detection |
|---|---|
| المؤلفون: | Hsu, Yi-Te, Zhu, Zining, Wang, Chi-Te, Fang, Shih-Hau, Rudzicz, Frank, Tsao, Yu |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning |
| الوصف: | 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. Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1811.10376Test |
| رقم الانضمام: | edsarx.1811.10376 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |