Automated Analysis of Digitized Letter Fluency Data.

The letter-guided naming fluency task is a measure of an individual's executive function and working memory. This study employed a novel, automated, quantifiable, and reproducible method to investigate how language characteristics of words produced during a fluency task are related to fluency performance, inter-word response time (RT), and over task duration using digitized F-letter-guided fluency recordings produced by 76 young healthy participants. Our automated algorithm counted the number of correct responses from the transcripts of the F-letter fluency data, and individual words were rated for concreteness, ambiguity, frequency, familiarity, and age of acquisition (AoA). Using a forced aligner, the transcripts were automatically aligned with the corresponding audio recordings. We measured inter-word RT, word duration, and word start time from the forced alignments. Articulation rate was also computed. Phonetic and semantic distances between two consecutive F-letter words were measured. We found that total F-letter score was significantly correlated with the mean values of word frequency, familiarity, AoA, word duration, phonetic similarity, and articulation rate; total score was also correlated with an individual's standard deviation of AoA, familiarity, and phonetic similarity. RT was negatively correlated with frequency and ambiguity of F-letter words and was positively correlated with AoA, number of phonemes, and phonetic and semantic distances. Lastly, the frequency, ambiguity, AoA, number of phonemes, and semantic distance of words produced significantly changed over time during the task. The method employed in this paper demonstrates the successful implementation of our automated language processing pipelines in a standardized neuropsychological task. This novel approach captures subtle and rich language characteristics during test performance that enhance informativeness and cannot be extracted manually without massive effort. This work will serve as the reference for letter-guided category fluency production similarly acquired in neurodegenerative patients.

Digital Speech Analysis in Progressive Supranuclear Palsy and Corticobasal Syndromes.

BACKGROUND: Progressive supranuclear palsy syndrome (PSPS) and corticobasal syndrome (CBS) as well as non-fluent/agrammatic primary progressive aphasia (naPPA) are often associated with misfolded 4-repeat tau pathology, but the diversity of the associated speech features is poorly understood. OBJECTIVE: Investigate the full range of acoustic and lexical properties of speech to test the hypothesis that PSPS-CBS show a subset of speech impairments found in naPPA. METHODS: Acoustic and lexical measures, extracted from natural, digitized semi-structured speech samples using novel, automated methods, were compared in PSPS-CBS (n = 87), naPPA (n = 25), and healthy controls (HC, n = 41). We related these measures to grammatical performance and speech fluency, core features of naPPA, to neuropsychological measures of naming, executive, memory and visuoconstructional functioning, and to cerebrospinal fluid (CSF) phosphorylated tau (pTau) levels in patients with available biofluid analytes. RESULTS: Both naPPA and PSPS-CBS speech produced shorter speech segments, longer pauses, higher pause rates, reduced fundamental frequency (f0) pitch ranges, and slower speech rate compared to HC. naPPA speech was distinct from PSPS-CBS with shorter speech segments, more frequent pauses, slower speech rate, reduced verb production, and higher partial word production. In both groups, acoustic duration measures generally correlated with speech fluency, measured as words per minute, and grammatical performance. Speech measures did not correlate with standard neuropsychological measures. CSF pTau levels correlated with f0 range in PSPS-CBS and naPPA. CONCLUSION: Lexical and acoustic speech features of PSPS-CBS overlaps those of naPPA and are related to CSF pTau levels.

Lexical and Acoustic Characteristics of Young and Older Healthy Adults.

Purpose This study examines the effect of age on language use with an automated analysis of digitized speech obtained from semistructured, narrative speech samples. Method We examined the Cookie Theft picture descriptions produced by 37 older and 76 young healthy participants. Using modern natural language processing and automatic speech recognition tools, we automatically annotated part-of-speech categories of all tokens, calculated the number of tense-inflected verbs, mean length of clause, and vocabulary diversity, and we rated nouns and verbs for five lexical features: word frequency, familiarity, concreteness, age of acquisition, and semantic ambiguity. We also segmented the speech signals into speech and silence and calculated acoustic features, such as total speech time, mean speech and pause segment durations, and pitch values. Results Older speakers produced significantly more fillers, pronouns, and verbs and fewer conjunctions, determiners, nouns, and prepositions than young participants. Older speakers' nouns and verbs were more familiar, more frequent (verbs only), and less ambiguous compared to those of young speakers. Older speakers produced shorter clauses with a lower vocabulary diversity than young participants. They also produced shorter speech segments and longer pauses with increased total speech time and total number of words. Lastly, we observed an interaction of age and sex in pitch ranges. Conclusions Our results suggest that older speakers' lexical content is less diverse, and these speakers produce shorter clauses than young participants in monologic, narrative speech. Our findings show that lexical and acoustic characteristics of semistructured speech samples can be examined with automated methods.