Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection

TL;DR

This paper introduces a deep learning framework that generates vocal quality embeddings for dysphonic voice detection, achieving high accuracy and robustness across different datasets and deteriorated conditions.

Contribution

It proposes a novel contrastive and classification loss combined deep learning model with data warping for robust vocal quality feature embeddings.

Findings

High in-corpus and cross-corpus classification accuracy

Embeddings sensitive to voice quality and robust across datasets

Consistently outperforms baseline methods on various datasets

Abstract

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.