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.

本研究提出了一种深度学习框架来生成对声音质量敏感且在不同语料库中具有鲁棒性的声学特征嵌入，同时将对比性损失与分类损失相结合进行训练，并使用数据膨胀方法提高模型的鲁棒性，实证结果表明，该方法不仅在语料库内和语料库间的分类准确度上表现出色，而且生成的嵌入对声音质量敏感且跨不同语料库具有鲁棒性。同时，与三种基准方法比较的结果表明，所提出的模型在干净以及损坏的语料库内和语料库间表现出一致的优异性能。