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Context-Aware Automatic Sign Language Video Transcription in Psychiatric Interviews.

Erion-Vasilis Pikoulis¹, Aristeidis Bifis¹, Maria Trigka¹

¹Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This study enhances sign language (SL) translation for mental health interviews by using domain knowledge to improve accuracy. Context-aware sentence retrieval significantly boosts performance in limited data scenarios.

Keywords:

machine learning sign language datasets sign language recognition

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Area of Science:

Computational linguistics
Artificial intelligence
Clinical psychology

Background:

Sign language translation is challenging, especially with limited data for specialized domains like mental health.
Deep learning models require extensive datasets, which are often unavailable for specific applications.
Prior information can significantly improve translation by narrowing down possibilities.

Purpose of the Study:

To improve sign language translation for psychiatric interviews involving deaf and hard of hearing patients.
To address the challenge of limited training data in specialized domains.
To leverage domain knowledge for enhanced translation accuracy.

Main Methods:

Developed a domain-specific approach for sign language translation in psychiatric settings.
Combined data-driven feature extraction with prior information from domain knowledge.
Utilized a hierarchical ontology to model interview context and classify interview states.
Treated video transcription as a sentence retrieval problem, predicting patient responses based on context.

Main Results:

Experimental evaluation demonstrated significant performance gains by incorporating context awareness.
The system successfully predicted signed patient sentences within simulated psychiatric interviews.
The domain-specific approach proved effective in overcoming data limitations.

Conclusions:

Context-aware sign language translation is crucial for improving accuracy in specialized domains like mental health.
Leveraging domain knowledge and hierarchical ontologies can effectively address data scarcity.
This approach offers a promising solution for improving communication in clinical settings for deaf and hard of hearing individuals.