How effective are multimodal systems in identifying and diagnosing speech, hearing, and language disorders in children?
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Multimodal systems show promise in identifying and diagnosing speech, hearing, and language disorders in children. These systems leverage various technologies like digital approaches, smart computing models, sensors, and automated analysis techniques to enhance screening, early detection, and diagnosis. Research indicates that utilizing high-level paralinguistic features and anomaly detection methods can aid in accurate detection of speech disorders, reducing the dependency on annotated data. Additionally, computer methods, including innovative techniques like computer animation, have shown effectiveness in diagnosing and correcting speech disorders in preschool children. Furthermore, advancements in natural language processing, such as Improved Conditional Random Fields, have been proposed to predict language impairments in children with high accuracy. These findings collectively highlight the potential of multimodal systems in improving the identification and diagnosis of speech, hearing, and language disorders in children.
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| Papers (5) | Insight |
|---|---|
21 Citations | Multimodal systems show promise in identifying speech disorders in children. Paralinguistic features and anomaly detection techniques achieved high accuracy in detecting speech disorders, offering effective diagnostic potential. |
| Multimodal systems, including computer methods and innovative techniques like computer animation, are effective in diagnosing and improving speech disorders in preschool and primary school children, as shown in the research. | |
| Multimodal systems, like the proposed game-based smart system, show promise in enhancing early detection of developmental speech/language disorders in children through digital approaches, sensors, and diagnostic indicators. | |
| Multimodal systems, like the Improved Conditional Random Fields (ICRF) proposed in the study, show promise in predicting Children Specifically Language Impairment (CSLI) severity levels with 89.14% accuracy. | |
1 Citations | Not addressed in the paper. |
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