Multimodal fusion-powered English speaking robot

Area of Science:

• Machine Learning
• Robotics
• Artificial Intelligence

Background:

• Multimodal learning and speech recognition are key AI areas.
• Existing systems face computational and complexity challenges.

Purpose of the Study:

• Introduce EnglishAL-Net, a novel multimodal fusion framework for English speaking robots.
• Optimize the ALBEF model for real-time speech and multimodal interaction.
• Integrate a text and image editor for enhanced visual and textual information fusion.

Main Methods:

• Leveraged the ALBEF model for multimodal fusion.
• Incorporated a novel text and image editor.
• Integrated Neural Machine Translation (NMT) for dynamic spoken input processing.

Main Results:

• EnglishAL-Net significantly improves language understanding accuracy compared to unimodal methods.
• The proposed model reduces response time in multimodal interactions.
• Experimental results validate the framework's effectiveness.

Conclusions:

• The EnglishAL-Net framework enhances robot multimodal interaction performance.
• This research opens new application possibilities for robotic technology.
• The study holds significant theoretical and practical value for AI and robotics.