Speech's syllabic rhythm and articulatory features produced under different auditory feedback conditions identify Parkinsonism

Abstract

Abstract Background Parkinsonism diagnostic tests based on speech samples have been reported with promising results. However, although abnormal auditory feedback integration during speech production and impaired rhythmic organization of speech have been shown in Parkinsonism, these observations have not been integrated into diagnostic tests. Objective To identify Parkinsonism and evaluate the power of a novel speech behavioral test (based on rhythmically repeating syllables under different auditory feedback conditions). Methods Thirty parkinsonism patients and thirty healthy subjects completed the study. Participants were instructed to repeat the PA-TA-KA syllable sequence rhythmically, whispering and speaking aloud under different listening conditions. The produced speech samples were preprocessed, and parameters were extracted. Classical, unpaired comparisons were conducted between patients and controls. Significant parameters were fed to a supervised machine-learning algorithm differentiating patients from controls, and the accuracy, specificity, and sensitivity were computed. Results Difficulties in whispering and articulating under altered auditory feedback conditions, delayed speech onset, and alterations in rhythmic stability were found in the group of patients compared to controls. A machine learning algorithm trained on these parameters to differentiate patients from controls reached an accuracy of 85.4%, a sensitivity of 87.8%, and a specificity of 83.1%. Conclusions The current work represents a pilot trial, showing the potential of the introduced behavioral paradigm as an objective and accessible (in cost and time) diagnostic test.