Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features an investigates their effectiveness in voice pathology detection. Glottal source features are extracted using glottal flows estimated with the quasi-closed phase (QCP) glottal inverse filtering method, using approximate glottal source signals computed with the zero frequency filtering (ZFF) method, and using acoustic voice signals directly. In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from the glottal source waveforms computed by QCP and ZFF to effectively capture the variations in glottal source spectra of pathological voice. Experiments were carried out using two databases, the Hospital Universitario Príncipe de Asturias (HUPA) database and the Saarbrücken Voice Disorders (SVD) database. Analysis of features revealed that the glottal source contains information that discriminates normal and pathological voice. Pathology detection experiments were carried out using support vector machine (SVM). From the detection experiments it was observed that the performance achieved with the studied glottal source features is comparable or better than that of conventional MFCCs and perceptual linear prediction (PLP) features. The best detection performance was achieved when the glottal source features were combined with the conventional MFCCs and PLP features, which indicates the complementary nature of the features.

Previous studies on the automatic classification of voice disorders have mostly investigated the binary classification task, which aims to distinguish pathological voice from healthy voice. Using multi-class classifiers, however, more fine-grained identification of voice disorders can be achieved, which is more helpful for clinical practitioners. Unfortunately, there is little publicly available training data for many voice disorders, which lowers the classification performance on data from unseen speakers. Earlier studies have shown that the usage of glottal source features can reduce data redundancy in detection of laryngeal voice disorders. Another approach to tackle the problems caused by scarcity of training data is to utilize deep learning models, such as wav2vec 2.0 and HuBERT, that have been pre-trained on larger databases. Since the aforementioned approaches have not been thoroughly studied in the multi-class classification of voice disorders, they will be jointly studied in the present work. In addition, we study a hierarchical classifier, which enables task-wise feature optimization and more efficient utilization of data. In this work, the aforementioned three approaches are compared with traditional mel frequency cepstral coefficient (MFCC) features and one-vs-rest and one-vs-one SVM classifiers. The results in a 3-class classification problem between healthy voice and two laryngeal disorders (hyperfunctional dysphonia and vocal fold paresis) indicate that all the studied methods outperform the baselines. The best performance was achieved by using features from wav2vec 2.0 LARGE together with hierarchical classification. The balanced classification accuracy of the system was 62.77% for male speakers, and 55.36% for female speakers, which outperformed the baseline systems by an absolute improvement of 15.76% and 6.95% for male and female speakers, respectively.

Automatic voice pathology detection is a research topic, which has gained increasing interest recently. Although methods based on deep learning are becoming popular, the classical pipeline systems based on a two-stage architecture consisting of a feature extraction stage and a classifier stage are still widely used. In these classical detection systems, frame-wise computation of mel-frequency cepstral coefficients (MFCCs) is the most popular feature extraction method. However, no systematic study has been conducted to investigate the effect of the MFCC frame length on automatic voice pathology detection. In this work, we studied the effect of the MFCC frame length in voice pathology detection using three disorders (hyperkinetic dysphonia, hypokinetic dysphonia and reflux laryngitis) from the Saarbrücken Voice Disorders (SVD) database. The detection performance was compared between speaker-dependent and speaker-independent scenarios as well as between speaking task -dependent and speaking task -independent scenarios. The Support Vector Machine, which is the most widely used classifier in the study area, was used as the classifier. The results show that the detection accuracy depended on the MFFC frame length in all the scenarios studied. The best detection accuracy was obtained by using a MFFC frame length of 500 ms with a shift of 5 ms.

Many acoustic features and machine learning models have been studied to build automatic detection systems to distinguish dysarthric speech from healthy speech. These systems can help to improve the reliability of diagnosis. However, speech recorded for diagnosis in real-life clinical conditions can differ from the training data of the detection system in terms of, for example, recording conditions, speaker identity, and language. These mismatches may lead to a reduction in detection performance in practical applications. In this study, we investigate the use of the wav2vec2 model as a feature extractor together with a support vector machine (SVM) classifier to build automatic detection systems for dysarthric speech. The performance of the wav2vec2 features is evaluated in two cross-database scenarios, language-dependent and language-independent, to study their generalizability to unseen speakers, recording conditions, and languages before and after fine-tuning the wav2vec2 model. The results revealed that the fine-tuned wav2vec2 features showed better generalization in both scenarios and gave an absolute accuracy improvement of 1.46% – 8.65% compared to the non-fine-tuned wav2vec2 features.