Voice Assessment of Speakers with Laryngeal Cancer by Glottal Excitation Modeling Based on a 2-Mass Model

The paper investigates the automatic evaluation of voice-related criteria of speakers with laryngeal cancer using a parametric two-mass model of the glottis. In contrast to previous approaches based on automatic speech recognition, the proposed method allows for a distinct evaluation of voice parameters alone since the underlying feature extraction technologies are based on a modeling of the whole vocal tract. This work focuses on the separation of vocal folds and vocal tract by LPC, where the vocal folds are represented by a parametric two-mass model which characterizes the excitation signal. The model parameters are optimized by a data-driven optimization procedure in order to fit the synthetic excitation signal to the LPC residue and the estimated pitch. We found first evidence that the computed parameters are meaningful in form of Pearson correlations between excitation signal parameters and different perceptual voice evaluation criteria in the range of r ≈ |0.7|.