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Human voice originates from the three-dimensional (3D) oscillations of the vocal folds. In previous studies, biomechanical properties of vocal fold tissues have been predicted by optimizing the parameters of simple two-mass-models to fit its dynamics to the high-speed imaging data from the clinic. However, only lateral and longitudinal displacements of the vocal folds were considered. To extend previous studies, a 3D mass-spring, cover-model is developed, which predicts the 3D vibrations of the entire medial surface of the vocal fold. The model consists of five mass planes arranged in vertical direction. Each plane contains five longitudinal, mass-spring, coupled oscillators. Feasibility of the model is assessed using a large body of dynamical data previously obtained from excised human larynx experiments, in vivo canine larynx experiments, physical models, and numerical models. Typical model output was found to be similar to existing findings. The resulting model enables visualization of the 3D dynamics of the human vocal folds during phonation for both symmetric and asymmetric vibrations.

With the use of an endoscopic, high-speed camera, vocal fold dynamics may be observed clinically during phonation. However, observation and subjective judgment alone may be insufficient for clinical diagnosis and documentation of improved vocal function, especially when the laryngeal disease lacks any clear morphological presentation. In this study, biomechanical parameters of the vocal folds are computed by adjusting the corresponding parameters of a three-dimensional model until the dynamics of both systems are similar. First, a mathematical optimization method is presented. Next, model parameters (such as pressure, tension and masses) are adjusted to reproduce vocal fold dynamics, and the deduced parameters are physiologically interpreted. Various combinations of global and local optimization techniques are attempted. Evaluation of the optimization procedure is performed using 50 synthetically generated data sets. The results show sufficient reliability, including 0.07 normalized error, 96% correlation, and 91% accuracy. The technique is also demonstrated on data from human hemilarynx experiments, in which a low normalized error (0.16) and high correlation (84%) values were achieved. In the future, this technique may be applied to clinical highspeed images, yielding objective measures with which to document improved vocal function of patients with voice disorders.

Within this study a retrospective analysis of clinical voice perturbation measures, Dysphonia Severity Index and subjective perceived hoarseness was performed to determine their value under clinical aspects. The study included the data of 580 healthy and 1,700 pathologic voices, which were investigated under the following aspects. The relevant parameters were identified and their interrelation determined. Group differences between healthy and pathologic voices were figured out and investigated if voice quality measures allowed an automatic diagnosis of voice disorders. The analysis revealed significant changes between the clinical groups, which indicate the diagnostic relevance of voice quality measures. However, an individual diagnosis of the underlying voice disorder failed due to a vast spread of the parameter values within the respective groups. Classification accuracies of 75-90% were achieved. The high misclassification rate of up to 25% implied that in voice disorder diagnosis, the individual interpretation of the parameter values has to be done carefully.