Early Visual Attention Abilities and Audiovisual Speech Processing in 5–7 Month-Old Down Syndrome and Typically Developing Infants

Pejovic, Jovana; Cruz, Marisa; Severino, Cátia; Frota, Sónia

doi:10.3390/brainsci11070939

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…This study compared Down syndrome (DS) and TD infants between the ages of 5 and 7 months in a visual orientation test as well as an audiovisual speech processing task, which examined infants’ gazing patterns to communicative signals (i.e., face, eyes, mouth, and waving arm) by Pejovic et al in 2021 [ 62 ]. The study found that DS infants’ early visual attention and audiovisual speech processing may be disrupted, with implications for their communication development, suggesting new options for early intervention in this clinical population.…”

Section: Discussionmentioning

confidence: 99%

Speech assessment tool methods for speech impaired children: a systematic literature review on the state-of-the-art in Speech impairment analysis

Usha

Alex

2023

Multimed Tools Appl

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Speech is a powerful, natural mode of communication that facilitates effective interactions in human societies. However, when fluency or flow of speech is affected or interrupted, it leads to speech impairment. There are several types of speech impairment depending on the speech pattern and range from mild to severe. Childhood apraxia of speech (CAS) is the most common speech disorder in children, with 1 out of 12 children diagnosed globally. Significant advancements in speech assessment tools have been reported to assist speech-language pathologists diagnosis speech impairment. In recent years, speech assessment tools have also gained popularity among pediatricians and teachers who work with preschoolers. Automatic speech tools can be more accurate for detecting speech sound disorders (SSD) than human-based speech assessment methods. This systematic literature review covers 88 studies, including more than 500 children, infants, toddlers, and a few adolescents, (both male and female) (age = 0–17) representing speech impairment from more than 10 countries. It discusses the state-of-the-art speech assessment methods, including tools, techniques, and protocols for speech-impaired children. Additionally, this review summarizes notable outcomes in detecting speech impairments using said assessment methods and discusses various limitations such as universality, reliability, and validity. Finally, we consider the challenges and future directions for speech impairment assessment tool research.

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Section: Discussionmentioning

confidence: 99%

Speech assessment tool methods for speech impaired children: a systematic literature review on the state-of-the-art in Speech impairment analysis

Usha

Alex

2023

Multimed Tools Appl

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“…The initial automated speech analysis (ASA) and recognition techniques developed during the 1960s and 1970s could handle isolated sounds from a minimal to moderate vocabulary (Pejovic et al, 2021). Linear predictive coding was devised to accommodate voice tract differences.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fine-Tuned CatBoost-Based Speech Disorder Detection Model

Dutta,

Wahab Sait

2024

Journal of Disability Research

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The classification of speech disorders (SDs) is crucial for treating children with speech impairment (SI). An automated SD classification can assist speech therapists in rendering services to children with SI in rural areas. Automated techniques for detecting SDs provide objective assessments of speech attributes, including articulation, fluency, and prosody. Clinical examinations and quantitative assessments provide an in-depth understanding of the patient’s speaking abilities and limitations. Existing deep learning (DL) models for SD detection often lack generalization across diverse populations and speech variations, leading to suboptimal performance when applied to individuals with different linguistic backgrounds or dialects. This study introduces a DL-based model for classifying normal and abnormal speeches using voice samples. To overcome the overfitting and bias, the authors construct convolutional neural network models with the weights of MobileNet V3 and EfficientNet B7 models for feature extraction (FE). To improve performance, they integrate the squeeze and excitation block with the MobileNet V3-based FE model. Similarly, the EfficientNet B7-model-based FE is improved using the structure pruning technique. The enhanced CatBoost model differentiates the normal and abnormal speeches using the extracted features. The experimental analysis is performed using the public dataset that contains 4620 utterances of healthy children and 2178 utterances of children with SI. The comparative study reveals the exceptional performance of the proposed SD classification model. The model outperforms the current SD classification models. It can be employed in clinical settings to support speech therapists. Substantial training with diverse voice samples can improve the generalizability of the proposed model.

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“…Individuals with DS are superior in sustained attention to response task (SART) than auditory SART (Trezise et al, 2008). According to Pejovic et al (2021), children with DS are slower in orienting their visual attention to stimuli, and distinct relationships are shown between the visual attention difficulties and poor literacy, numeracy, and social skills (Erik et al, 2016). Therefore, it is suggested that the development of physical environment factors and presentation of educational materials in visual medium may facilitate improved attention of children with DS.…”

Section: Sustained Attention In Children With Ds: Visual Vs Auditorymentioning

confidence: 99%

Impact of Greenery in the Window View on Visual Sustained Attention with Special Reference to Classrooms of Children with Down Syndrome

Nushara,

Hettiarachchi

2023

JRES

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One in every 700 babies worldwide is born with an intellectual disability such as Down Syndrome (DS), Autism Spectrum disorders, Cerebral Palsy etc. Architecture significantly enhances users’ development skills by leveraging the environment to fulfill their mental and psychological needs, particularly by boosting sustained attention in learning spaces. Based on existing literature, this research focused on the impact of greenery in window view of the classroom on visual sustained attention (VSA) of children with DS. Data of 19 participants (10 male, 9 female) of a selected school in the ages of 9 – 16 years (mean age – 11.42 years) was collected on existing and introduced levels of greenery in window view using three dimensional simulations and analysed. The study revealed that the introduction of 40% to 60% of greenery at far view depth (Scenario B) has a positive impact on VSA over both the existing window view (Scenario A) and the window view with 80% to 100% greenery (Scenario C). However, scenario C did not show any positive impact over scenario B as assumed. One reason for this could be the inclusion of the sky view in scenario B, and the findings indicate that introduction of greenery in the window view has a positive impact on VSA of children with DS, if the sky view is maintained. These findings can be used towards a design framework for classrooms in the future contexts of education in Sri Lanka.

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Early Visual Attention Abilities and Audiovisual Speech Processing in 5–7 Month-Old Down Syndrome and Typically Developing Infants

Cited by 5 publications

References 42 publications

Speech assessment tool methods for speech impaired children: a systematic literature review on the state-of-the-art in Speech impairment analysis

Speech assessment tool methods for speech impaired children: a systematic literature review on the state-of-the-art in Speech impairment analysis

A Fine-Tuned CatBoost-Based Speech Disorder Detection Model

Impact of Greenery in the Window View on Visual Sustained Attention with Special Reference to Classrooms of Children with Down Syndrome

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