Disfluency Assessment Using Deep Super Learners

The use of machine learning algorithms for the assessment of speech fluency is increasingly becoming recognized globally due to their ability to quickly identify speech impairments. This approach is preferred over manual diagnosis, as it reduces the likelihood of human error and minimizes the delay in commencing the therapy. A pipelined deep learner-dual classifier (PDL-DC) is proposed for the automated detection of speech impairment. The assessment of individuals’ speech fluency consisted of two distinct phases: the classification of speech disfluencies and the categorization of fluency disorders. Speech disfluencies, including revisions, prolongations, whole-word repetitions, word-medial repetitions, and filled pauses, were categorized into distinct groupings. The second aspect of classification pertains to the assessment of fluency levels, wherein speakers are classified into three categories: healthy individuals, individuals with stuttering, and individuals with Specific Language Impairment (SLI). The proposed model’s implementation of a pipelined design enables the dual validation of a subject’s fluency. The proposed model demonstrates an average classification accuracy, precision, and recall of 97%.