Zaznavanje hipofonije iz posnetkov govora bolnikov s Parkinsonovo boleznijo

Parkinson's disease affects approximately 10 million people worldwide. With the recent advancements in the development of neuroprotective drugs aimed at slowing or halting the progression of PD, early diagnosis has become increasingly important. These early diagnostic methods are split between invasive and usually costly methods (DatScan, biomarker, and genetic testing) and noninvasive methods (gait analysis, olfactory function, emotion recognition, and voice analysis). Our research focused on detecting PD using voice analysis, which can theoretically be done quickly and remotely, thus being accessible to more people. We have created a new dataset with 24 control subjects and 9 patients with PD. The dataset comprised of demographical data (age, gender, and education), results from psychological tests (GDS-15, MMSE, FAB, and MDS-UDPRS III), and recordings of participants reading short compositions (three neutral compositions and six compositions with emotional content). Using this dataset, we tried to classify the subjects based on their reading accuracy, detected emotions in their speech, and extracted MFCC features. We have shown that using reading accuracy, we were able to correctly classify 6 out of 9 experimental subjects and all of the control subjects. Similarly, using the MFCC features of words containing 2 or more syllables, we could classify all subjects correctly. Unfortunately, we could not draw any conclusions using detected emotions from the subject's speech, as the used emotion detection model predicted most of the recordings to express a neutral emotion while labeling voice recordings from males with deeper voices as sad. This study strengthened the results from the previous research and showed that voice analysis could serve as a viable, cost-effective, and noninvasive method for detecting PD.