System Identification Algorithms Applied to Glottal Model Fitting

This study proposes a new method of fitting a glottal model to the glottal flow estimate using system identification (SI) algorithms. Each period of the glottal estimate is split into open and closed phases and each phase is modelled as the output of a linear filter. This approach allows the parametric model fitting task to be cast as a system identification problem and sidesteps issues encountered with standard glottal parametrisation algorithms. The study compares the performance of two SI methods: Steiglitz-McBride and Prony. The tests were performed on synthetic glottal signals (n=121) and real speech (n=50 healthy, n=23 pathological). The effectiveness of the techniques is quantified by calculating the Normalised Root Mean Squared Error (NRMSE) between the estimated glottal fit and the glottal estimate. Tests on synthetic glottal signals show that the average performance of the Steiglitz-McBride method (97.25%) was better than the Prony method (70.41%). Real speech tests produced results of 64.29% and 51.57% for healthy and pathological speech respectively. The results show that system identification techniques can produce robust parametric model estimates of the glottal waveform and that the Steiglitz-McBride method is superior to the Prony method for this task.