Echo | Devpost

Echo: Bridging Signs and Sounds

Inspirations

We were inspired by the growing need for accessible communication tools that empower the deaf and hard-of-hearing community. Our goal was to create a solution that fosters inclusivity and breaks communication barriers in real-time.

What it does

Echo converts sign language videos into captions and audio, enabling seamless interaction between sign language users and non-signers. The tool bridges the gap by providing instant, accessible translations.

How we built it

We developed Echo using a combination of computer vision and natural language processing techniques. Leveraging fine-trained model for sign language recognition, we processed videos to identify gestures, translate them into text, and synthesize audio output. The backend was built in Python, while the frontend interface is designed for ease of use with NextJS/React.

Challenges we ran into

Fine-tuning sign language recognition models to improve accuracy and reduce latency.
Managing real-time processing with limited computational resources.
Ensuring translations captured the nuances and context of sign language effectively.

Accomplishments that we’re proud of

Successfully building a working prototype that translates sign language to text and speech in real-time.
Overcoming challenges with model optimization to achieve lower latency.
Creating a user-friendly interface to make the tool accessible to all.

What we learned

How to optimize computer vision models for real-time applications.
Launching and deploying machine learning models.
How to use NextJS and fastAPI to make a working web app ### What's next for Echo
Expanding support for additional sign languages to cater to a global audience.
Enhancing translation accuracy and adding contextual awareness for more natural conversations.
Integrating with applications like zoom, microsoft teams and google meet