Detecting Cognitive Load during Working Memory Tasks utilizing a Digitizer Tablet

The purpose of this line of research is to determine whether the ‘Digitizer’ is a reliable and valid way to measure cognitive load during dual working memory-drawing tasks. A quasi-experimental study was conducted at the University of Arkansas in a research laboratory, and participants included seven right-handed healthy adults with normal or corrected vision and no reading difficulty. The participants were selected on a volunteer basis. The study required participants to draw circles while continuously performing in three conditions – one baseline and two working memory experimental tasks, administered in counterbalanced order. The baseline task was to read an 8th grade level passage at comfortable speed and loudness level. The working memory tasks were symmetry span and operation span tasks. The operation span task required the participants to remember letters in sequence while simultaneously verifying arithmetic operations presented after each letter. The symmetry span task required participants to remember the position of the highlighted square in a grid in sequence while simultaneously determining the symmetricity of a figure presented afterwards. Both tasks were completed while drawing continuous circles on the ‘Digitizer’. A separate repeated measures analysis of variance (ANOVA) was conducted for each measure. A significant omnibus effect was found for the stroke duration measure only. Post-hoc paired tests showed that baseline was higher (p=.01) in stroke duration than in operation span task and symmetry span task. In this literature review, the results and elements of the study are described in full to inform future research. It was initially assumed that the working memory load would be significantly less in the baseline task as compared to the two working memory tasks; however, the data alternatively indicated that it taxed working memory more. With reading comprehension as a reference condition, it is logical to conclude that there is evidence of cognitive load in working memory tasks as measured by manual disfluencies. This literature review outlines potential adaptations and highlights primary weaknesses for future study in this area

Detecting stuttering events in transcripts of children’s speech

Stuttering is a common problem in childhood that may persist into adulthood if not treated in early stages. Techniques from spoken language understanding may be applied to provide automated diagnosis of stuttering from children speech. The main challenges however lie in the lack of training data and the high dimensionality of this data. This study investigates the applicability of machine learning approaches for detecting stuttering events in transcripts. Two machine learning approaches were applied, namely HELM and CRF. The performance of these two approaches are compared, and the effect of data augmentation is examined in both approaches. Experimental results show that CRF outperforms HELM by 2.2% in the baseline experiments. Data augmentation helps improve systems performance, especially for rarely available events. In addition to the annotated augmented data, this study also adds annotated human transcriptions from real stuttered children’s speech to help expand the research in this field

Anomalous morphology in left hemisphere motor and premotor cortex of children who stutter

Stuttering is a neurodevelopmental disorder that affects the smooth flow of speech production. Stuttering onset occurs during a dynamic period of development when children first start learning to formulate sentences. Although most children grow out of stuttering naturally, ∼1% of all children develop persistent stuttering that can lead to significant psychosocial consequences throughout one’s life. To date, few studies have examined neural bases of stuttering in children who stutter, and even fewer have examined the basis for natural recovery versus persistence of stuttering. Here we report the first study to conduct surface-based analysis of the brain morphometric measures in children who stutter. We used FreeSurfer to extract cortical size and shape measures from structural MRI scans collected from the initial year of a longitudinal study involving 70 children (36 stuttering, 34 controls) in the 3–10-year range. The stuttering group was further divided into two groups: persistent and recovered, based on their later longitudinal visits that allowed determination of their eventual clinical outcome. A region of interest analysis that focused on the left hemisphere speech network and a whole-brain exploratory analysis were conducted to examine group differences and group × age interaction effects. We found that the persistent group could be differentiated from the control and recovered groups by reduced cortical thickness in left motor and lateral premotor cortical regions. The recovered group showed an age-related decrease in local gyrification in the left medial premotor cortex (supplementary motor area and and pre-supplementary motor area). These results provide strong evidence of a primary deficit in the left hemisphere speech network, specifically involving lateral premotor cortex and primary motor cortex, in persistent developmental stuttering. Results further point to a possible compensatory mechanism involving left medial premotor cortex in those who recover from childhood stuttering.This study was supported by Award Numbers R01DC011277 (SC) and R01DC007683 (FG) from the National Institute on Deafness and other Communication Disorders (NIDCD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the National Institutes of Health. (R01DC011277 - National Institute on Deafness and other Communication Disorders (NIDCD); R01DC007683 - National Institute on Deafness and other Communication Disorders (NIDCD))Accepted manuscrip

The level of stuttering severity among students with learning disabilities in English language

The study aimed at identifying the extent of the prevalence of behaviors associated with the phenomenon of stuttering and the degree of its severity among students with learning disabilities in English in the basic stage and its relationship with variables of age and gender. The sample of the study consisted of 310 female and male students from 100 schools in Irbid city, Jordan, in the academic year 2019/2020. The study used a test to measure the the levels of stuttering among students. The results of the study showed that the prevalence of behaviors associated with stuttering of all degrees of severity, simple, medium, and severe, was 0.51% of the study sample. The degree of prevalence of behaviors associated with stuttering was more in males than in females, and the degree of moderate stuttering severity constituted the highest percentage in the study sample. The results also showed that there were no statistically significant differences in the degree of severity of stuttering among primary school children with learning disabilities in English due to the variables of age and gender

Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving speech recognition systems for people with such non-typical speech or tracking the effectiveness of speech therapy would require systems that can detect dysfluencies while at the same time being able to detect speech techniques acquired in therapy. This paper shows that fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features for detecting stuttering in speech; both within and across languages. We evaluate our method on FluencyBank , [2] and the German therapy-centric Kassel State of Fluency (KSoF) [3] dataset by training Support Vector Machine classifiers using features extracted from the finetuned models for six different stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. Using embeddings from the fine-tuned models leads to relative classification performance gains up to 27% w.r.t. F1-score.Comment: Accepted at Interspeech 202

End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge

In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifically the stuttering sub-challenge. In particular, we exploit the embeddings from the pre-trained Wav2Vec2.0 model for stuttering detection (SD) on the KSoF dataset. After embedding extraction, we benchmark with several methods for SD. Our proposed self-supervised based SD system achieves a UAR of 36.9% and 41.0% on validation and test sets respectively, which is 31.32% (validation set) and 1.49% (test set) higher than the best (DeepSpectrum) challenge baseline (CBL). Moreover, we show that concatenating layer embeddings with Mel-frequency cepstral coefficients (MFCCs) features further improves the UAR of 33.81% and 5.45% on validation and test sets respectively over the CBL. Finally, we demonstrate that the summing information across all the layers of Wav2Vec2.0 surpasses the CBL by a relative margin of 45.91% and 5.69% on validation and test sets respectively. Grand-challenge: Computational Paralinguistics ChallengEComment: Accepted in ACM MM 2022 Conference : Grand Challenges, "\c{opyright} {Owner/Author | ACM} {2022}. This is the author's version of the work. It is posted here for your personal use. Not for redistributio