Ep. 28: How Syed Ahmed Taught AI to Translate Sign Language
Jun 28, 2017
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Syed Ahmed, an undergraduate student, is pioneering the use of AI to bridge communication gaps for the deaf community. He discusses developing a deep learning model that translates American Sign Language into English. The conversation explores innovative technologies aiding sign language users, the challenges of optimizing translations, and the importance of diverse datasets. Syed also highlights the ongoing need for research to improve communication effectiveness and the versatile applications of AI in visual information translation.
Syed Ahmed's deep learning model translates American Sign Language into English, enhancing communication for the deaf and hearing populations.
The initiative leverages over 17,000 video-caption pairs to train the model, addressing challenges in processing speed for real-time interactions.
Deep dives
Innovative Communication Solutions for the Deaf
A new deep learning model has been developed to translate American Sign Language (ASL) into English, aiming to bridge communication gaps between the deaf and hearing populations. This model allows users to point their phones at a signer, enabling real-time automatic captioning of signed communication. By leveraging technology such as wearable devices, this system functions similarly to Google Translate but specifically focuses on visual language, addressing the limitations of current communication methods for the deaf community. The initiative arose from the need for a more effective way to communicate beyond typing or writing, offering a more inclusive approach to interaction.
Challenges of Video Processing and Model Training
Training the ASL translation model involves parsing sequences from video frames to capture the nuances of sign language, such as hand movements and facial expressions. The process relies on a dataset of over 17,000 videos paired with captions, allowing the model to learn iteratively how to interpret various signs. However, significant challenges remain, particularly regarding processing speed and minimizing lag time to facilitate real-time conversations. Improvements in mobile processing power and optimization techniques, such as those available in TensorFlow, are being explored to enhance the system's efficiency.
Potential Applications Beyond Sign Language
The principles behind the ASL translation model could extend to other applications, such as video summarization and pose detection. For instance, users could query a film for specific scenes without manually searching, while pose detection could have implications in medical assessments by monitoring gait and predicting health issues. These applications illustrate how translating visual information into actionable insights could benefit various fields, including entertainment and healthcare. The overarching aim is to develop systems that facilitate seamless communication and provide critical information across different contexts.
We all know how far AI, and in particular deep learning, have pushed speech recognition, whether that is with Apple Siri, Amazon Alexa, or Google Assistant, Our guest on this segment, Syed Ahmed, is directing the power of AI towards another form of communication, American Sign Language. And what
Syed has done is set up a deep learning model that translates American Sign Language into the English Language.
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