Abstract
The most common way for people to communicate is by means of talking. Some people suffer from voice disorder because of overusing the vocal folds or due to other vocalfold pathology. In an ear-nose-throat clinic, ways of diagnosing the vocal-fold pathology include listening to patients’ voice and watching the movement of patients’ vocal folds. The former method is performed through recording and then analyzing the acoustic parameters to examine whether phonation is normal or not. This method is objective. Alternatively, a subjective way is for the doctor to grade the condition of patients’ voice directly through hearing, and these evaluation indexes include Grade, Roughness, Breathiness, Aesthenia and Strain (GRBAS). The latter method is using stroboscopy to observe the dynamic variation of vocal folds, then the doctor evaluates whether the appearance and movement of vocal folds are normal. Stroboscopy is usually used by doctors to judge the severity of vocal-fold damage subjectively. There is currently no standard way to automate this process. Therefore, we attempt to quantify common glottal physiological parameters (GPPs) including Aperiodicity, Opening Quotient (OQ), Symmetry and Roughness by image processing. The ultimate purpose of this research is to use acoustic parameters to predict the vocal-fold damage severity observed from stroboscopic images. Our motivation was based on the fact that stroboscopy is an invasive instrument which could hurt the patient. In this research, we collected the recording voice files and acoustic parameters including Jitter, Shimmer and Noise-to-Harmonic Ratio (NHR) and also collected the data of GRBAS from 15 patients before they went through vocal-fold surgery. Then we used the linear prediction method to analyze the voice files and calculated their prediction error signals. Finally we calculated the correlation function of the prediction error signals and defined a parameter called Pitch Amplitude (PA). Then combinations of these parameters were used to construct a linear model that gives the best prediction of GPPs in terms of least-square approximation. The R-square, which is used to evaluate the performance of each GPP, can reach Aperiodicity=0.907, OQ=0.774, Symmetry=0.783 and Roughness=0.833. The GPPs can be predicted well by the linear combination of the acoustic parameters which is a non-invasive method.