Automated Analysis of Digitized Letter Fluency Data

Frontiers in Psychology

article

Sunghye Cho¹  Naomi Nevler²  Natalia Parjane²  Christopher Cieri¹  Mark Liberman¹  Murray Grossman²  Katheryn A. Q. Cousins² 

[1] University of Pennsylvania, United States;Penn Frontotemporal Degeneration Center, University of Pennsylvania, United States

关键词: neuropsychological test;    automated speech analysis;    verbal retrieval;    executive function;    verbal fluency;    phonetic similarity;   

DOI  :  10.3389/fpsyg.2021.654214

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY