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People with hearing and speaking disabilities face significant hurdles in communication. The knowledge of sign language can help mitigate these hurdles, but most people without disabilities, including relatives, friends, and care providers, cannot understand sign language. The availability of automated tools can allow people with disabilities and those around them to communicate ubiquitously and in a variety of situations with non-signers. There are currently two main approaches for recognizing sign language gestures. The first is a hardware-based approach, involving gloves or other hardware to track hand position and determine gestures. The second is a software-based approach, where a video is taken of the hands and gestures are classified using computer vision techniques. However, some hardware, such as a phone's internal sensor or a device worn on the arm to track muscle data, is less accurate, and wearing them can be cumbersome or uncomfortable. The software-based approach, on the other hand, is dependent on the lighting conditions and on the contrast between the hands and the background. We propose a hybrid approach that takes advantage of low-cost sensory hardware and combines it with a smart sign-recognition algorithm with the goal of developing a more efficient gesture recognition system. The Myo band-based approach using the Support Vector Machine method achieves an accuracy of only 49%. The software-based approach uses the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods to train the Myo-based module and achieves an accuracy of over 80% in our experiments. Our method combines the two approaches and shows the potential for improvement. Our experiments are done with a dataset of nine gestures generated from multiple videos, each repeated five times for a total of 45 trials for both the software-based and hardware-based modules. Apart from showing the performance of each approach, our results show that with a more improved hardware module, the accuracy of the combined approach can be significantly improved
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Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens
Much of the world’s population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against people with disabilities, i.e., prejudice that favors those with typical abilities. We report on various analyses based on word predictions of a large-scale BERT language model. Statistically significant results demonstrate that people with disabilities can be disadvantaged. Findings also explore overlapping forms of discrimination related to interconnected gender and race identities.
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- Award ID(s):
- 1763569
- PAR ID:
- 10335690
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Page Range / eLocation ID:
- 3116 to 3123
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2021.findings-emnlp.267
