English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Speech disorders such as stuttering disrupt the normal fluency of speech byinvoluntary repetitions, prolongations and blocking of sounds and syllables. Inaddition to these disruptions to speech fluency, most adults who stutter (AWS)also experience numerous observable secondary behaviors before, during, andafter a stuttering moment, often involving the facial muscles. Recent studieshave explored automatic detection of stuttering using Artificial Intelligence(AI) based algorithm from respiratory rate, audio, etc. during speechutterance. However, most methods require controlled environments and/orinvasive wearable sensors, and are unable explain why a decision (fluent vsstuttered) was made. We hypothesize that pre-speech facial activity in AWS,which can be captured non-invasively, contains enough information to accuratelyclassify the upcoming utterance as either fluent or stuttered. Towards thisend, this paper proposes a novel explainable AI (XAI) assisted convolutionalneural network (CNN) classifier to predict near future stuttering by learningtemporal facial muscle movement patterns of AWS and explains the importantfacial muscles and actions involved. Statistical analyses reveal significantlyhigh prevalence of cheek muscles (p<0.005) and lip muscles (p<0.005) to predictstuttering and shows a behavior conducive of arousal and anticipation to speak.The temporal study of these upper and lower facial muscles may facilitate earlydetection of stuttering, promote automated assessment of stuttering and haveapplication in behavioral therapies by providing automatic non-invasivefeedback in realtime.

Stuttering Speech Disfluency Prediction using Explainable Attribution Vectors of Facial Muscle Movements