Lotto, Andrew J.

High-density electroencephalography (EEG) was used to evaluate cortical activity patterns during the auditory processing of speech presented at various levels of degradation in a sentence verification task. 25 healthy participants listened to true-false sentences produced with one of 3 channel levels (1, 6, 16), ranging from unintelligible, moderately intelligible, and highly intelligible. Behavioral data were collected via button press (reaction time and accuracy) for each sentence. The analysis of cortical activation patterns includes 1) the identification of independent components that account for variations of activity associated with degradation levels and 2) the examination of associated event-related potentials. Statistical analyses will be performed on individuals' cortical activity, in tandem with behavioral data. Preliminary results reveal differences in the timing and magnitude of frontal and temporal lobe activation that coincide with task difficulty. Results of the present investigation will inform hypotheses about regions of interest for further investigation and will bear on models of connected speech perception, including the neurobiological underpinnings of individual differences and manifestations in a listener's understanding of degraded speech.

Purpose In this study, the authors examined whether rhythm metrics capable of distinguishing languages with high and low temporal stress contrast also can distinguish among control and dysarthric speakers of American English with perceptually distinct rhythm patterns. Methods Acoustic measures of vocalic and consonantal segment durations were obtained for speech samples from 55 speakers across 5 groups (hypokinetic, hyperkinetic, flaccid-spastic, ataxic dysarthrias, and controls). Segment durations were used to calculate standard and new rhythm metrics. Discriminant function analyses (DFAs) were used to determine which sets of predictor variables (rhythm metrics) best discriminated between groups (control vs. dysarthrias; and among the 4 dysarthrias). A cross-validation method was used to test the robustness of each original DFA. Results The majority of classification functions were more than 80% successful in classifying speakers into their appropriate group. New metrics that combined successive vocalic and consonantal segments emerged as important predictor variables. DFAs pitting each dysarthria group against the combined others resulted in unique constellations of predictor variables that yielded high levels of classification accuracy. Conclusions This study confirms the ability of rhythm metrics to distinguish control speech from dysarthrias and to discriminate dysarthria subtypes. Rhythm metrics show promise for use as a rational and objective clinical tool.