Search Results (51)

Background and Objectives: Although psychiatric disorders in early childhood are increasingly recognized, comprehensive clinical data from large samples in this age group remain limited. This study presents one of the largest and longest-term evaluations in Türkiye of children aged 0–72 months referred to child psychiatry. It aims to identify the most common presenting complaints, diagnostic patterns, and key predictors of psychotropic medication initiation in a previously understudied age group. Materials and Methods: This retrospective analysis included 3312 children aged 0–72 months who presented to the outpatient child psychiatry clinic of Trakya University Medical Faculty Hospital in Edirne, Türkiye. Clinical records were reviewed to extract data on presenting complaints, psychiatric diagnoses, psychotropic medication initiation, and demographic details, including age and sex. Results: The most common presenting complaints were “delayed speech development”, “irritability/frustration”, “hyperactivity”, “requests for medical reports”, and “stuttering.” These complaints were more prevalent among children who received psychiatric diagnoses. Psychiatric diagnoses were more common in boys. Boys also presented at older ages and had longer follow-up durations. Psychotropic medications were initiated in 26.9% of the cases. The most frequently reported side effects were loss of appetite and drowsiness. Logistic regression analysis revealed that specific complaints were significantly predictive of initiating medication. These included “inability to speak”, “irritability/frustration”, “hyperactivity”, “lack of eye contact”, “aggression”, “school refusal”, “sleep problems”, and “fears.” Conclusions: This study revealed that some early childhood complaints, such as “inability to speak”, “restlessness”, “hyperactivity”, and “not making eye contact”, are strong predictors of both psychiatric diagnosis and initiation of psychotropic medication. The findings highlight the usefulness of structured assessment protocols in early childhood psychiatric services. The implementation of systematic screening for risk symptoms may facilitate early diagnosis and support more appropriate and timely treatment approaches, especially in resource-limited regions. Full article

Background/Objectives: Disfluencies such as repetitions, prolongations, interjections, and blocks in sounds, syllables, or words can sometimes hinder communication. Currently, disfluencies are manually measured, which has inherent limitations, such as being time-consuming and subjective, which can lead to inconsistencies in measurement. Methods: To address these challenges, this study presents an innovative automated system for detecting disfluencies utilizing advanced artificial intelligence technologies; specifically, deep learning models such as convolutional neural networks (CNN) and convolutional long short-term memory (ConvLSTM). The system was evaluated using two benchmark datasets: FluencyBank and SEP-28K. Results: Our proposed system demonstrates remarkable performance, achieving detection accuracies of 0.97 and 0.96, respectively, for CNNs and ConvLSTM models. These results not only exceed those of prior studies but also highlight the effectiveness of our approach in enhancing stuttering evaluation. Conclusions: By providing a reliable and efficient tool for professionals in therapeutic settings, our system represents a significant advancement in the field, offering improved outcomes for individuals affected by stuttering. Full article

Objectives: Early difficulties in emotion regulation are associated with psychopathological, broader social, and developmental outcomes, underscoring the need for robust assessment tools at a young age. However, most of the existing instruments for preschoolers measure emotion regulation in general, without focusing on specific emotion regulation strategies. This study addresses a critical gap by validating a Dutch version of the Early Emotion Regulation Behavior Questionnaire (EERBQ), enabling researchers and practitioners to assess preschoolers’ emotion regulation strategies in both positive- as well as negative-emotion-eliciting situations outside of laboratory settings. Methods: Through a rigorous back-translation process, the parental questionnaire was adapted into Dutch (EERBQ-Dutch) and subsequently validated with a sample of 299 Dutch-speaking caregivers of typically developing 2–7-year-old children. The test underwent psychometric analysis including inter-item correlations, item–total correlations, test–retest reliability, and confirmatory factor analysis. Finally, potential sociodemographic predictors (i.e., age, sex, and socioeconomic status (SES)) of specific emotion regulation strategies were investigated. Results: Psychometric analyses demonstrated strong reliability and validity, and a factor structure consistent with the original English questionnaire. Age and sex were found to be significant predictors of certain emotion regulation strategies, with more proficient use of adaptive emotion regulation strategies over time and girls employing more Verbal Help-Seeking and less Physical Venting and Reactivity compared to boys. SES only contributed to Emotional Reactivity with a higher SES predicting more Reactivity. Conclusions: Our findings support the EERBQ-Dutch as a reliable and culturally appropriate instrument for assessing early emotion regulation and provide insight into key predictors of emotion regulation strategies. Full article

Punctuation restoration plays an essential role in the postprocessing procedure of automatic speech recognition, but model efficiency is a key requirement for this task. To that end, we present EfficientPunct, an ensemble method with a multimodal time-delay neural network that outperforms the current best model by 1.0 F1 point while using less than a tenth of its network parameters for inference. This work further streamlines a speech recognizer and a BERT implementation to efficiently output hidden layer acoustic embeddings and text embeddings in the context of punctuation restoration. Here, forced alignment and temporal convolutions are used to eliminate the need for attention-based fusion, greatly increasing computational efficiency and improving performance. EfficientPunct sets a new state of the art with an ensemble that weighs BERT’s purely language-based predictions slightly more than the multimodal network’s predictions. Although EfficientPunct shows great promise, from a different perspective, to date, another important challenge in the field has been the fact that punctuation restoration models have been evaluated almost solely on well-structured, scripted corpora. However, real-world ASR systems and postprocessing pipelines typically apply to spontaneous speech with significant irregularities, stutters, and deviations from perfect grammar. To address this important discrepancy, we also introduce SponSpeech, a punctuation restoration dataset derived from informal speech sources, which includes punctuation and casing information. In addition to publicly releasing the dataset, the authors have contributed by providing a filtering pipeline that can be used to generate more data. This filtering pipeline examines the quality of both the speech audio and the transcription text. A challenging test set is also carefully constructed, aimed at evaluating the models’ ability to leverage audio information to predict, otherwise grammatically ambiguous, punctuation. SponSpeech has been made available to the public, along with all code for dataset building and model runs. Full article

Background/Objectives: Stuttering is a speech disorder involving fluency disruptions like repetitions, prolongations, and blockages, often leading to emotional distress and social withdrawal. Here, we present Augmented Multisensory Feedback Stimulation (AMFS), a novel personalized intervention to improve speech fluency in people who stutter (PWS). AMFS includes a five-day intensive phase aiming at acquiring new skills, plus a reinforcement phase designed to facilitate the transfer of these skills across different contexts and their automatization into effortless behaviors. The concept of our intervention derives from the prediction of the neurocomputational model Directions into Velocities of Articulators (DIVA). The treatment applies dynamic multisensory stimulation to disrupt PWS’ maladaptive over-reliance on sensory feedback mechanisms, promoting the emergence of participants’ natural voices. Methods: Forty-six PWS and a control group, including twenty-four non-stuttering individuals, participated in this study. Stuttering severity and physiological measures, such as heart rate and electromyographic activity, were recorded before and after the intensive phase and during the reinforcement stage in the PWS but only once in the controls. Results: The results showed a significant reduction in stuttering severity at the end of the intensive phase, which was maintained during the reinforcement training. Crucially, worse performance was found in PWS than in the controls at baseline but not after the intervention. In the PWS, physiological signals showed a reduction in activity during the training phases compared to baseline. Conclusions: Our findings show that AMFS provides a promising approach to enhancing speech fluency. Future studies should clarify the mechanisms underlying such intervention and assess whether effects persist after the treatment conclusion. Full article

Speech disorders encompass a complex interplay of neuroanatomical, genetic, and environmental factors affecting individuals’ communication ability. This review synthesizes current insights into the neuroanatomy, genetic underpinnings, and environmental influences contributing to speech disorders. Neuroanatomical structures, such as Broca’s area, Wernicke’s area, the arcuate fasciculus, and basal ganglia, along with their connectivity, play critical roles in speech production, comprehension, and motor coordination. Advances in the understanding of intricate brain networks involved in language offer insights into typical speech development and the pathophysiology of speech disorders. Genetic studies have identified key genes involved in neural migration and synaptic connectivity, further elucidating the role of genetic mutations in speech disorders, such as stuttering and speech sound disorders. Beyond the biological mechanisms, this review explores the profound impact of psychological factors, including anxiety, depression, and neurodevelopmental conditions, on individuals with speech disorders. Psychosocial comorbidities often exacerbate speech disorders, complicating diagnosis and treatment and underscoring the need for a holistic approach to managing these conditions. Future directions point toward leveraging genetic testing, digital technologies, and personalized therapies, alongside addressing the psychosocial dimensions, to improve outcomes for individuals with speech disorders. This comprehensive overview aims to inform future research and therapeutic advancements, particularly in treating fluency disorders like stuttering. Full article

This conceptual paper critically examines the use of traditional medicalized terminology in speech-language therapy, with a particular focus on the Quebec context. It highlights how current language practices, rooted in a medical model of disability, often marginalize individuals with communication differences such as stuttering, autism, and aphasia by pathologizing these variations. Drawing on contemporary frameworks such as the social model of disability, neurodiversity, and “diversité capacitaire” (a French term that translates to “capacity diversity” or “ability diversity”, emphasizing the richness of diverse abilities and communication styles), the article advocates for more inclusive and empowering language that respects and reflects communicative diversity. The authors emphasize the importance of participatory approaches, including consultation with the communities directly involved and the establishment of terminological committees, to develop respectful and affirming language. Ultimately, this paper calls for a shift in speech-language therapy practices to promote a more inclusive understanding of communication, enabling individuals with communication differences to fully participate in society. Full article

A plentiful number of facial expressions is the basis of natural human–robot interaction for high-fidelity humanoid robots. The facial expression imitation of humanoid robots involves the transmission of human facial expression data to servos situated within the robot’s head. These data drive the servos to manipulate the skin, thereby enabling the robot to exhibit various facial expressions. However, since the mechanical transmission rate cannot keep up with the data processing rate, humanoid robots often suffer from jitters in the imitation. We conducted a thorough analysis of the transmitted facial expression sequence data and discovered that they are extremely redundant. Therefore, we designed a two-stage strategy for humanoid robots based on facial keyframe detection and facial keypoint detection to achieve more natural and smooth expression imitation. We first built a facial keyframe detection model based on ResNet-50, combined with optical flow estimation, which can identify key expression frames in the sequence. Then, a facial keypoint detection model is used on the keyframes to obtain the facial keypoint coordinates. Based on the coordinates, the cubic spline interpolation method is used to obtain the motion trajectory parameters of the servos, thus realizing the robust control of the humanoid robot’s facial expression. Experiments show that, unlike before where the robot’s imitation would stutter at frame rates above 25 fps, our strategy allows the robot to maintain good facial expression imitation similarity (cosine similarity of 0.7226), even at higher frame rates. Full article

Differences in sensorimotor integration mechanisms have been observed between people who stutter (PWS) and controls who do not. Delayed auditory feedback (DAF) introduces timing discrepancies between perception and action, disrupting sequence production in verbal and non-verbal domains. While DAF consistently enhances speech fluency in PWS, its impact on non-verbal sensorimotor synchronization abilities remains unexplored. A total of 11 PWS and 13 matched controls completed five tasks: (1) unpaced tapping; (2) synchronization-continuation task (SCT) without auditory feedback; (3) SCT with DAF, with instruction either to align the sound in time with the metronome; or (4) to ignore the sound and align their physical tap to the metronome. Additionally, we measured participants’ sensitivity to detecting delayed feedback using a (5) delay discrimination task. Results showed that DAF significantly affected performance in controls as a function of delay duration, despite being irrelevant to the task. Conversely, PWS performance remained stable across delays. When auditory feedback was absent, no differences were found between PWS and controls. Moreover, PWS were less able to detect delays in speech and tapping tasks. These findings show subtle differences in non-verbal sensorimotor performance between PWS and controls, specifically when action–perception loops are disrupted by delays, contributing to models of sensorimotor integration in stuttering. Full article

Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals’ quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5–15 Hz range to identify significant differences. Upon analysis of the 5–15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering. Full article

Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored. By examining the existing literature, we investigated the application of AI in accurately determining and classifying stuttering manifestations. Furthermore, we explored how computational intelligence can contribute to developing innovative assessment tools and intervention strategies for persons who stutter (PWS). We reviewed and analyzed 14 refereed journal articles that were indexed on the Web of Science from 2019 onward. The potential of AI and computational intelligence in revolutionizing stuttering assessment and treatment, which can enable personalized and effective approaches, is also highlighted in this review. By elucidating these advancements, we aim to encourage further research and development in this crucial area, enhancing in due course the lives of PWS. Full article

The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using “minutes”, “hours”, and “days” as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of “minutes”, “hours”, and “days”, the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model’s retrieval efficiency also outperforms the space–time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed. Full article

Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The researchers introduce “TranStutter”, a pioneering Convolution-free Transformer-based DL model, designed to excel in speech disfluency classification. Unlike conventional methods, TranStutter leverages Multi-Head Self-Attention and Positional Encoding to capture intricate temporal patterns, yielding superior accuracy. In this study, the researchers employed two benchmark datasets: the Stuttering Events in Podcasts Dataset (SEP-28k) and the FluencyBank Interview Subset. SEP-28k comprises 28,177 audio clips from podcasts, meticulously annotated into distinct dysfluent and non-dysfluent labels, including Block (BL), Prolongation (PR), Sound Repetition (SR), Word Repetition (WR), and Interjection (IJ). The FluencyBank subset encompasses 4144 audio clips from 32 People Who Stutter (PWS), providing a diverse set of speech samples. TranStutter’s performance was assessed rigorously. On SEP-28k, the model achieved an impressive accuracy of 88.1%. Furthermore, on the FluencyBank dataset, TranStutter demonstrated its efficacy with an accuracy of 80.6%. These results highlight TranStutter’s significant potential in revolutionizing the diagnosis and treatment of stuttering, thereby contributing to the evolving landscape of speech pathology and neurodevelopmental research. The innovative integration of Multi-Head Self-Attention and Positional Encoding distinguishes TranStutter, enabling it to discern nuanced disfluencies with unparalleled precision. This novel approach represents a substantial leap forward in the field of speech pathology, promising more accurate diagnostics and targeted interventions for individuals with stuttering disorders. Full article

Background: Gabapentin (GBP)-induced movement disorders (MDs) are under-recognized adverse drug reactions. They are commonly not discussed with patients, and their sudden occurrence can lead to misdiagnosis. This literature review aims to evaluate the clinical–epidemiological profile, pathological mechanisms, and management of GBP-associated MD. Methods: Two reviewers identified and assessed relevant reports in six databases without language restriction between 1990 and 2023. Results: A total of 99 reports of 204 individuals who developed a MD associated with GBP were identified. The MDs encountered were 135 myoclonus, 22 dyskinesias, 7 dystonia, 3 akathisia, 3 stutterings, 1 myokymia, and 1 parkinsonism. The mean and median ages were 54.54 (SD: 17.79) and 57 years (age range: 10–89), respectively. Subjects were predominantly male (53.57%). The mean and median doses of GBP when the MD occurred were 1324.66 (SD: 1117.66) and 1033 mg/daily (GBP dose range: 100–9600), respectively. The mean time from GBP-onset to GBP-associated MD was 4.58 weeks (SD: 8.08). The mean recovery time after MD treatment was 4.17 days (SD: 4.87). The MD management involved GBP discontinuation. A total of 82.5% of the individuals had a full recovery in the follow-up period. Conclusions: Myoclonus (GRADE A) and dyskinesia (GRADE C) were the most common movement disorders associated with GBP. Full article

The sexual health of young people during the youth age period is of utmost importance, as it sets the stage for sexual well-being over the life course. In addition to the existing challenges that youth face concerning their sexual health, electronic cigarettes may also negatively impact their sexual well-being. Specifically, through issues such as stuttering priapism, reduced sperm quality and quantity, and erectile dysfunction. Electronic cigarette use among youth is prevalent. Therefore, given the negative sexual health impacts associated with electronic cigarette use, coupled with the prevalent use of electronic cigarettes, it is necessary for youth to receive adequate support and guidance, so they understand the potential impacts that electronic cigarette use can have on their sexual well-being. Urologists are uniquely situated to play an important role in supporting the sexual health of youth, given their medical and surgical knowledge, however, it appears that they do not receive adequate training to carry out discussions about sexual health with youth. This paper aims to support urologists to have discussions with youth patients on the impact that electronic cigarettes have on their sexual health through a proposed four-step framework. This four-step framework involves: (i) establishing the relationship, (ii) assessing current electronic cigarette use, (iii) sharing research examining the impact of electronic cigarettes on sexual health, and (iv) discussing strategies to prevent/reduce or stop electronic cigarette use. It is necessary to acknowledge that this framework is only a small component of efforts to educate youth on the impacts that electronic cigarettes have on their sexual health. Moving forward, implementation and evaluation of this framework is needed. Full article