• Corpus ID: 245669001

Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach

@article{Chi2022ClassifyingAF,
  title={Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach},
  author={Nathan Chi and Peter Yigitcan Washington and Aaron Kline and Arman Husic and Cathy Hou and Chloe He and Kaitlyn Dunlap and Dennis Paul Wall},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.00927}
}
Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results in altered behavior, social development, and communication patterns. In past years, autism prevalence has tripled, with 1 in 54 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process which requires the work of trained physicians, significant attention has been given to developing systems that automatically diagnose and screen for autism. Objective: Prosody… 

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References

SHOWING 1-10 OF 67 REFERENCES

Automatic Detection of Autism Spectrum Disorder in Children Using Acoustic and Text Features from Brief Natural Conversations

TLDR
The goal of this project is to develop an automatic detection system for ASD that relies on very brief, generic, and natural conversations, which can eventually be used for ASD prescreening and triage in realworld settings such as doctor’s offices and schools.

Mobile detection of autism through machine learning on home video: A development and prospective validation study

TLDR
The hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames is supported, using mobile devices.

Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation

TLDR
A pre-trained feature extraction auto-encoder model and a joint optimization scheme are introduced which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models.

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study

TLDR
These results show promise for using a mobile video-based and machine learning–directed approach for early and remote detection of autism in Bangladeshi children and identify the population-level burden of developmental disabilities and impairments.

Child vocalization composition as discriminant information for automatic autism detection

TLDR
It is discovered that child vocalization composition contains rich discriminant information for autism detection, and it is believed that this new tool can serve a significant role in childhood autism screening, especially in regards to population-based or universal screening.

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study

TLDR
Commercial emotion classifiers available off-the-shelf to the general public through Microsoft, Amazon, Google, and Sighthound are well-suited to the pediatric population and could be used for developing mobile therapies targeting aspects of social communication and interaction, perhaps accelerating innovation in this space.

Leveraging video data from a digital smartphone autism therapy to train an emotion detection classifier

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting one in 40 children in the United States and is associated with impaired social interactions, restricted interests, and

Feature Selection and Dimension Reduction of Social Autism Data

TLDR
This work performed item-level question selection on answers to the Social Responsiveness Scale, Second Edition to determine whether ASD can be distinguished from non-ASD using a similarly small subset of questions, and evaluated the performance of an MLP classifier trained on the low-dimension representations of the SRS.

Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism

TLDR
This feasibility study supports the utility of a wearable device for social affective learning in ASD children and demonstrates subtle differences in how ASD and NC children perform on an emotion recognition task.
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