Are Speech Attractor Models Useful in Diagnosing Vocal Fold Pathologies?

Development of non-invasive methods in diagnosing different diseases can lead to improvement of prevention and care programs. The speech is an easily accessible signal, which clearly represents the characteristics of larynx and vocal folds. Therefore, application of some proper machine learning algorithms (e.g., feature extraction and classification methods) on a small part of a recorded speech signal may help in diagnosing vocal fold diseases such as paralysis, edema, nodules, and polyp. Generally, transforming the input signal into the set of features is called feature extraction. If these features are accurately extracted, it is expected that the feature set will capture the relevant pathological information of speech signal to predict the diagnosis.