Acoustic Predictors of Ease of Understanding in Spanish Speakers With Dysarthria Associated With Parkinson's Disease.

PURPOSE: The purpose of this study was to examine selected baseline acoustic features of hypokinetic dysarthria in Spanish speakers with Parkinson's disease (PD) and identify potential acoustic predictors of ease of understanding in Spanish. METHOD: Seventeen Spanish-speaking individuals with mild-to-moderate hypokinetic dysarthria secondary to PD and eight healthy controls were recorded reading a translation of the Rainbow Passage. Acoustic measures of vowel space area, as indicated by the formant centralization ratio (FCR), envelope modulation spectra (EMS), and articulation rate were derived from the speech samples. Additionally, 15 healthy adults rated ease of understanding of the recordings on a visual analogue scale. A multiple linear regression model was implemented to investigate the predictive value of the selected acoustic parameters on ease of understanding. RESULTS: Listeners' ease of understanding was significantly lower for speakers with dysarthria than for healthy controls. The FCR, EMS from the first 10 s of the reading passage, and the difference in EMS between the end and the beginning sections of the passage differed significantly between the two groups of speakers. Findings indicated that 67.7% of the variability in ease of understanding was explained by the predictive model, suggesting a moderately strong relationship between the acoustic and perceptual domains. CONCLUSIONS: Measures of envelope modulation spectra were found to be highly significant model predictors of ease of understanding of Spanish-speaking individuals with hypokinetic dysarthria associated with PD. Articulation rate was also found to be important (albeit to a lesser degree) in the predictive model. The formant centralization ratio should be further examined with a larger sample size and more severe dysarthria to determine its efficacy in predicting ease of understanding.

Beyond Speech Intelligibility: Quantifying Behavioral and Perceived Listening Effort in Response to Dysarthric Speech.

PURPOSE: This study investigated whether listener processing of dysarthric speech requires the recruitment of more cognitive resources (i.e., higher levels of listening effort) than neurotypical speech. We also explored relationships between behavioral listening effort, perceived listening effort, and objective measures of word transcription accuracy. METHOD: A word recall paradigm was used to index behavioral listening effort. The primary task involved word transcription, whereas a memory task involved recalling words from previous sentences. Nineteen listeners completed the paradigm twice, once while transcribing dysarthric speech and once while transcribing neurotypical speech. Perceived listening effort was rated using a visual analog scale. RESULTS: Results revealed significant effects of dysarthria on the likelihood of correct word recall, indicating that the transcription of dysarthric speech required higher levels of behavioral listening effort relative to neurotypical speech. There was also a significant relationship between transcription accuracy and measures of behavioral listening effort, such that listeners who were more accurate in understanding dysarthric speech exhibited smaller changes in word recall when listening to dysarthria. The subjective measure of perceived listening effort did not have a statistically significant correlation with measures of behavioral listening effort or transcription accuracy. CONCLUSIONS: Results suggest that cognitive resources, particularly listeners' working memory capacity, are more taxed when deciphering dysarthric versus neurotypical speech. An increased demand on these resources may affect a listener's ability to remember aspects of their conversations with people with dysarthria, even when the speaker is fully intelligible.

Predicting Montreal Cognitive Assessment Scores From Measures of Speech and Language.

Purpose This study examined the relationship between measurements derived from spontaneous speech and participants' scores on the Montreal Cognitive Assessment. Method Participants (N = 521) aged between 64 and 97 years completed the cognitive assessment and were prompted to describe an early childhood memory. A range of acoustic and linguistic measures was extracted from the resulting speech sample. A least absolute shrinkage and selection operator approach was used to model the relationship between acoustic, lexical, and demographic information and participants' scores on the cognitive assessment. Results Using the covariance test statistic, four important variables were identified, which, together, explained 16.52% of the variance in participants' cognitive scores. Conclusions The degree to which cognition can be accurately predicted through spontaneously produced speech samples is limited. Statistically significant relationships were found between specific measurements of lexical variation, participants' speaking rate, and their scores on the Montreal Cognitive Assessment.

Predicting Intelligibility Gains in Dysarthria Through Automated Speech Feature Analysis.

Purpose: Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, & Liss, 2017). This study reexamines these features and assesses whether automated acoustic assessments can also be used to predict intelligibility gains. Method: Fifty speakers (7 older individuals and 43 with dysarthria) read a passage in habitual, loud, and slow speaking modes. Automated measurements of long-term average spectra, envelope modulation spectra, and Mel-frequency cepstral coefficients were extracted from short segments of participants' baseline speech. Intelligibility gains were statistically modeled, and the predictive power of the baseline speech measures was assessed using cross-validation. Results: Statistical models could predict the intelligibility gains of speakers they had not been trained on. The automated acoustic features were better able to predict speakers' improvement in the loud condition than the manual measures reported in the companion article. Conclusions: These acoustic analyses present a promising tool for rapidly assessing treatment options. Automated measures of baseline speech patterns may enable more selective inclusion criteria and stronger group outcomes within treatment studies.