Analyzing the Disfluency of Reading Tasks of Persons Who Stutter Based on Deep Learning and Word Embedding

Objectives: Recent natural language processing systems employ embedding techniques, which convert linguistic expressions into numerical vectors in order to measure the geometric distance between expressions. Using skills and focusing on the reading tasks, the present study aims to reveal the distributional properties of disfluencies. Methods: The current work segmented the reading data of 110 adolescents and adults who stutter, transformed the data into a vector space, and then conducted the embedding calculation. Utilizing Word2Vec, the cosine similarity was measured so as to look at how the types of disfluencies were co-related to each other. Results: The eight ND (Normal disfluencies) and AD (Abnormal disfluencies) types, excluding the R2 (Repetition 2) and DP (Disrhythmic Phonation) types, were close to each other with respect to the cosine similarity (>.9). In particular, the AD types such as Ha (Abnormal hesitation), Ia (Abnormal interjection), URa (Abnormal unfinished/revision word), and R1a (Abnormal Repetition1) largely overlapped with each other. R2 and DP showed different distributional properties from other types of disfluencies. The results also indicated that each ND and AD pair seldom differed in their distributional properties. Finally, this study it found that several consonants tended to appear more often when the speakers produced disfluencies. Conclusion: This study draws the distributional patterns of fluency disorders in an automatic way using deep learning skills. The findings are of use for the diagnosis and treatment of the fluency disorders.