Speech Metrics and Samples That Differentiate Between Nonfluent/Agrammatic and Logopenic Variants of Primary Progressive Aphasia
- PMID: 33630653
- PMCID: PMC8608203
- DOI: 10.1044/2020_JSLHR-20-00445
Speech Metrics and Samples That Differentiate Between Nonfluent/Agrammatic and Logopenic Variants of Primary Progressive Aphasia
Abstract
Purpose Of the three currently recognized variants of primary progressive aphasia, behavioral differentiation between the nonfluent/agrammatic (nfvPPA) and logopenic (lvPPA) variants is particularly difficult. The challenge includes uncertainty regarding diagnosis of apraxia of speech, which is subsumed within criteria for variant classification. The purpose of this study was to determine the extent to which a variety of speech articulation and prosody metrics for apraxia of speech differentiate between nfvPPA and lvPPA across diverse speech samples. Method The study involved 25 participants with progressive aphasia (10 with nfvPPA, 10 with lvPPA, and five with the semantic variant). Speech samples included a word repetition task, a picture description task, and a story narrative task. We completed acoustic analyses of temporal prosody and quantitative perceptual analyses based on narrow phonetic transcription and then evaluated the degree of differentiation between nfvPPA and lvPPA participants (with the semantic variant serving as a reference point for minimal speech production impairment). Results Most, but not all, articulatory and prosodic metrics differentiated statistically between the nfvPPA and lvPPA groups. Measures of distortion frequency, syllable duration, syllable scanning, and-to a limited extent-syllable stress and phonemic accuracy showed greater impairment in the nfvPPA group. Contrary to expectations, classification was most accurate in connected speech samples. A customized connected speech metric-the narrative syllable duration-yielded excellent to perfect classification accuracy. Discussion Measures of average syllable duration in multisyllabic utterances are useful diagnostic tools for differentiating between nfvPPA and lvPPA, particularly when based on connected speech samples. As such, they are suitable candidates for automatization, large-scale study, and application to clinical practice. The observation that both speech rate and distortion frequency differentiated more effectively in connected speech than on a motor speech examination suggests that it will be important to evaluate interactions between speech and discourse production in future research.
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