A Transfer Learning Approach for the 2018 FEMH Voice Data Challenge

Human voice could be significantly affected by neoplasm, vocal palsy, and phono-trauma diseases. Computer aided diagnosis by analyzing human voice can be a remote and cost-effective tool for patients around the world. In this paper, we propose a deep transfer learning approach to differentiate pathological voice samples from normal ones. We utilize voice samples recorded from 200 patients at the Far Eastern Memorial Hospital (FEMH) to develop the deep transfer learning model. We extract prosodic, vocal tract and excitation features as new representations from the voice samples for diagnosis. To address the small data set challenge, we utilize the TIMIT dataset and develop a transfer learning approach in which a deep belief network (DBN) is first trained with the TIMIT data set. The trained model is then applied to the FEMH data set as a feature extractor. Finally, we train a support vector machine (SVM) classifier with the extracted features for diagnosis. We evaluate our approach using the leave one out cross validation (LOOCV) strategy on the 200 training patients, and achieve 94.90% sensitivity with 59.77% un-weighted average recall (UAR) for the 400 FEMH testing patients. Our results prove that the proposed method may be used effectively for pathological voice detection.