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Neha Tarigopula

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Publications associées (4)

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Advancing Phonology-Based Sign Language Assessment: From Learner to Machine-Generated Videos

Neha Tarigopula

Sign languages are rich visual languages that use both manual features like handshape, movement, and location and non-manual features such as facial expressions, head movements, and body posture to convey information. Sign language learning technologies ha ...

EPFL2025

Content-Based Objective Evaluation of Artificially Generated Sign Language Videos

Neha Tarigopula

Sign language is vital for communication within the deaf and hard-of-hearing community. Avatar-based methods and deep learning techniques like Generative Adversarial Networks have shown promise in generating sign language video content. One of the challeng ...

IEEE2024

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Neha Tarigopula

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Towards Accessible Sign Language Assessment and Learning

Neha Tarigopula

Recently, a phonology-based sign language assessment approach has been proposed using sign language production acquired in 3D space using Kinect sensor. In order to scale the sign language assessment system to realistic application, there is need to reduce ...

New York2022