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- All Subjects: Speech Perception
- All Subjects: Articulation disorders
- Creators: Boothroyd, Arthur
Description
Distorted vowel production is a hallmark characteristic of dysarthric speech, irrespective of the underlying neurological condition or dysarthria diagnosis. A variety of acoustic metrics have been used to study the nature of vowel production deficits in dysarthria; however, not all demonstrate sensitivity to the exhibited deficits. Less attention has been paid to quantifying the vowel production deficits associated with the specific dysarthrias. Attempts to characterize the relationship between naturally degraded vowel production in dysarthria with overall intelligibility have met with mixed results, leading some to question the nature of this relationship. It has been suggested that aberrant vowel acoustics may be an index of overall severity of the impairment and not an "integral component" of the intelligibility deficit. A limitation of previous work detailing perceptual consequences of disordered vowel acoustics is that overall intelligibility, not vowel identification accuracy, has been the perceptual measure of interest. A series of three experiments were conducted to address the problems outlined herein. The goals of the first experiment were to identify subsets of vowel metrics that reliably distinguish speakers with dysarthria from non-disordered speakers and differentiate the dysarthria subtypes. Vowel metrics that capture vowel centralization and reduced spectral distinctiveness among vowels differentiated dysarthric from non-disordered speakers. Vowel metrics generally failed to differentiate speakers according to their dysarthria diagnosis. The second and third experiments were conducted to evaluate the relationship between degraded vowel acoustics and the resulting percept. In the second experiment, correlation and regression analyses revealed vowel metrics that capture vowel centralization and distinctiveness and movement of the second formant frequency were most predictive of vowel identification accuracy and overall intelligibility. The third experiment was conducted to evaluate the extent to which the nature of the acoustic degradation predicts the resulting percept. Results suggest distinctive vowel tokens are better identified and, likewise, better-identified tokens are more distinctive. Further, an above-chance level agreement between nature of vowel misclassification and misidentification errors was demonstrated for all vowels, suggesting degraded vowel acoustics are not merely an index of severity in dysarthria, but rather are an integral component of the resultant intelligibility disorder.
ContributorsLansford, Kaitlin L (Author) / Liss, Julie M (Thesis advisor) / Dorman, Michael F. (Committee member) / Azuma, Tamiko (Committee member) / Lotto, Andrew J (Committee member) / Arizona State University (Publisher)
Created2012
Description
The purpose of the present study was to determine if an automated speech perception task yields results that are equivalent to a word recognition test used in audiometric evaluations. This was done by testing 51 normally hearing adults using a traditional word recognition task (NU-6) and an automated Non-Word Detection task. Stimuli for each task were presented in quiet as well as in six signal-to-noise ratios (SNRs) increasing in 3 dB increments (+0 dB, +3 dB, +6 dB, +9 dB, + 12 dB, +15 dB). A two one-sided test procedure (TOST) was used to determine equivalency of the two tests. This approach required the performance for both tasks to be arcsine transformed and converted to z-scores in order to calculate the difference in scores across listening conditions. These values were then compared to a predetermined criterion to establish if equivalency exists. It was expected that the TOST procedure would reveal equivalency between the traditional word recognition task and the automated Non-Word Detection Task. The results confirmed that the two tasks differed by no more than 2 test items in any of the listening conditions. Overall, the results indicate that the automated Non-Word Detection task could be used in addition to, or in place of, traditional word recognition tests. In addition, the features of an automated test such as the Non-Word Detection task offer additional benefits including rapid administration, accurate scoring, and supplemental performance data (e.g., error analyses) beyond those obtained in traditional speech perception measures.
ContributorsStahl, Amy Nicole (Author) / Pittman, Andrea (Thesis director) / Boothroyd, Arthur (Committee member) / McBride, Ingrid (Committee member) / School of Human Evolution and Social Change (Contributor) / Department of Speech and Hearing Science (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05