One of the key features of the hypokinetic dysarthria associated with Parkinson disease is dysprosody. While there has been ample research into the global characterization of speech in Parkinson disease, little is known about how people with Parkinson disease mark lexical stress. This study aimed to determine how people with Parkinson disease modulate pitch, intensity, duration, and vowel space to differentiate between two common lexical stress patterns in English: trochees (strong-weak pattern) and iambs (weak-strong pattern), in two syllable words. Twelve participants with mild to moderate idiopathic Parkinson disease and twelve age- and sex-matched controls completed a series of speech tasks designed to elicit token words of interest in prosodically-relevant speech tasks (picture identification (in isolation and lists) and giving directions (spontaneous speech). Results revealed that people with Parkinson disease produced a higher overall pitch and a smaller vowel space as compared to controls, though most lexical marking features were not significantly different. Importantly, the elicitation task had a significant effect on most dependent measures. Although lexical stress is not significantly impacted by Parkinson disease, we recommend that future research and clinical practice focus more on the use of spontaneous speech tasks rather than isolated words or lists of words due to the differences in the marking of lexical stress in the latter tasks, making them less useful as ecologically-valid assessments of prosody in everyday communication.

This work deals with designing and testing of new acoustic features for analysis of dysprosodic speech occurring in hypokinetic dysarthria patients. 41 new features for dysprosody quantification (describing melody, loudness, rhythm and pace) are presented and tested in this work. New features can be divided into 7 groups. Inside the groups, features vary by the used statistical values. First four groups are based on absolute differences and cumulative sums of fundamental frequency and short-time energy of the signal. Fifth group contains features based on multiples of this fundamental frequency and short-time energy combined into one global intonation feature. Sixth group contains global time features, which are made of divisions between conventional rhythm and pace features. Last group contains global features for quantification of whole dysprosody, made of divisions between global intonation and global time features. All features were tested on Czech Parkinsonian speech database PARCZ. First, kernel density estimation was made and plotted for all features. Then correlation analysis with medicinal metadata was made, first for all the features, then for global features only. Next classification and regression analysis were made, using classification and regression trees algorithm (CART). This analysis was first made for all the features separately, then for all the data at once and eventually a sequential floating feature selection was made, to find out the best fitting combination of features for the current matter. Even though none of the features emerged as a universal best, there were a few features, that were appearing as one of the best repeatedly and also there was a trend that there was a bigger drop between the best and the second best feature, marking it as a much better feature for the given matter, than the rest of the tested. Results are included in the conclusion together with the discussion.