Sign Language Recognition: The Journey and Challenges Ahead
If communication is a fundamental aspect of human behavior that compels us to interact with each other to live in society by continuing social bonds, disabled people like the deaf community resort to non-verbal means like sign language to satiate this need for interaction and to navigate their daily lives. The onset of AI technologies has opened up exciting vistas in this arena by offering improved prospects of accessibility and inclusion.
Sign language technology ushered in by AI, such as the real-time translation of sign languages into spoken languages, is important because it bridges the gap between the deaf community and the hearing world, thereby allowing for greater integration of the deaf community with the non-disabled world, instead of forcing them into isolating silos. This would allow them to participate in the everyday functioning of non-disabled society by having them included within settings such as education, healthcare, as well as social spheres. For example, by allowing the deaf community to avail Further, digital tools also aid in the creation of inclusive online content on social media which can be consumed by the deaf community, thereby empowering them to avail information as well as entertainment avenues seamlessly.
There is also an emerging cluster of wearable technologies that aim to remove the communication barrier between those who use sign language and those who do not. For example, gloves are embedded with sensors that can track and gauge the hand motion of the wearer who is communicating via sign language. Not only are these technologies vital for human-human interaction (between signers and non-signers) by offering real-time translation, but they are also used for purposes of human-computer interaction. Through functioning as assistive platforms for sign-language users to enact complicated commands directly, sign-language recognition and translation technologies are eroding the communication barriers that compartmentalize the non-disabled public from the speech and hearing-impaired communities.
The use of AI-enabled data analytics to achieve high-quality translation of sign languages, however, still has a long way to go, as current technologies are still limited by the threshold of only being able to translate discrete words, instead of being able to translate full sentences. Thus, they offer limited use as of now in meeting the daily requirements of sign language users, who are still unable to mediate their daily lives independently by virtue of seamless communication with verbal language users.
Here, advanced research proposes the use of technologies that synthesize motion sensors, with AI technologies and back-end virtual-reality interfaces to move beyond the ability of being able to only pare single words. This method proposes the ability to translate complete sentences through a segmentation method that divides sentence signals into word fragments, and translates them independently, as the AI learns, memorizes, and translates all of the split elements in order to finally yield an output with a reconstructed and meaningful complete sentence. Therefore, we need more robust research in this direction for a more reliable and high-precision interaction between signers and non-signers.
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