As of right now, our largest risk remains to be working HPE on the FPGA, but as mentioned before, we are currently in the process of developing that and we have a backup plan of running HPE on the Jetson if necessary. The other major risk as of right now is our RNN model and whether a standard GRU architecture is enough. While we do believe it should be, we have planned for this in being able to switch out GRU cells with LSTM cells, using new architectures such as MUSE-RNN, and considering the possibility of moving to transformers if necessary.<\/p>\n
As of right now, there has been no significant change in the design plans of our project. We have options to pivot in the case of a competent failure or lack of performance, as mentioned above, but we have not yet had to commit to one of these pivots.<\/p>\n
With consideration of global factors, our biggest impact will be on the deaf and hard of hearing community – particularly the ones who use the American Sign Language (ASL) and are reliant on the digital video communication platforms like zoom. For all those individuals, it is quite a challenge to participate in online platforms like zoom and have to rely on either chat features or a translator if other people in the meeting are not familiar with ASL. Our project helps those individuals globally by removing that reliance and bringing accessibility in communication. This will bring a greater, more effective form of communication to hundreds of thousands of ASL users across the globe.<\/p>\n
Our\u00a0product solution has substantial implications for the way the hearing world can engage with ASL users in virtual spaces. Culturally, hearing impaired communities tend to be forgotten about and separate from the hearing world in America. Influence in public spaces, like concerts,\u00a0 and legislation has been hard to come by and accommodations tend to be fought for rather than freely given. Part of this issue stems from hearing communities not having personal relationships with or awareness of the hearing impaired. Every step towards cultivating spaces where deaf individuals can comfortably participate in any and all discussions is a step towards hearing impaired communities having a louder voice in the cultural factors of the communities they identify with.\u00a0An ASL to text feature in video communication is a small step towards meeting the needs of hearing impaired communities, but opens the doorway to substantial progress. By creating <\/span>avenues\u00a0of direct <\/span>interpersonal\u00a0communication (without the need for a dedicated translator in every spoken space), the potential for an array of meaningful <\/span>relationships\u00a0in <\/span>professional, academic, and\u00a0<\/span>recitational\u00a0spaces opens up.\u00a0<\/span><\/p>\n While an ASL translation system for digital environments aims to improve accessibility and inclusion for the deaf and hard of hearing community, it does not directly address core environmental needs or sustainability factors. The computational requirements for running machine learning models can be energy-intensive, so our system design will prioritize efficient neural architectures and lean cloud\/edge deployment to minimize environmental impact from an energy usage perspective. Additionally, by increasing accessibility to online information, this technology could potentially help promote environmental awareness and education within the Deaf community as an indirect bonus. However, as a communication tool operating in the digital realm, this ASL translator does not inherently solve specific environmental issues and its impact is confined to the human-centric domains of accessibility. While environmental sustainability is not a primary focus, we still aim for an efficient system that has a minimal environmental footprint.<\/span><\/p>\n As of right now, our largest risk remains to be working HPE on the FPGA, but as mentioned before, we are currently in the process of developing that and we have a backup plan of running HPE on the Jetson if necessary. The other major risk as of right now is our RNN model and … <\/p>\n\n