  Approximately 1.2% of the world's population has impaired voice production.
As a result, automatic dysphonic voice detection has attracted considerable
academic and clinical interest. However, existing methods for automated voice
assessment often fail to generalize outside the training conditions or to other
related applications. In this paper, we propose a deep learning framework for
generating acoustic feature embeddings sensitive to vocal quality and robust
across different corpora. A contrastive loss is combined with a classification
loss to train our deep learning model jointly. Data warping methods are used on
input voice samples to improve the robustness of our method. Empirical results
demonstrate that our method not only achieves high in-corpus and cross-corpus
classification accuracy but also generates good embeddings sensitive to voice
quality and robust across different corpora. We also compare our results
against three baseline methods on clean and three variations of deteriorated
in-corpus and cross-corpus datasets and demonstrate that the proposed model
consistently outperforms the baseline methods.
