All Technologies Used
Motivation
The primary goal was to create a system that leverages machine learning to recognize and analyze voice commands from restaurant staff and customers. This includes converting speech into text, extracting commands, and processing tasks efficiently while supporting multiple languages and resource-limited hardware.
Main Challenges
The system required proficiency in multiple languages, including English, Spanish, and French. Mechanisms for recognizing and processing diverse language-based requests were designed to cater to multilingual customers.
Customers and employees needed the freedom to issue voice commands in natural language without adhering to strict formats. The system was trained to identify and interpret informal commands seamlessly.
The customer requested a solution deployable on weak hardware, such as laptops. Optimizing the ML model for resource efficiency was a critical challenge in ensuring local deployment feasibility.
Key Features
- Multilingual Capabilities: Supports multiple languages, enabling restaurants to cater to diverse clientele.
- Natural Voice Interaction: Allows customers and staff to issue commands in natural language, enhancing user experience and workflow efficiency.
- Optimized for Low-Resource Deployment: Designed for local deployment on low-power devices, making the system accessible for a broader range of customers.
- Task Automation and Monitoring: Automates task creation and tracking while providing real-time status updates on dashboards and staff devices.
Our Approach
Project Impact
The developed system streamlines restaurant operations by automating task management and improving communication between customers and staff.
Enhanced multilingual support and resource-efficient deployment enable restaurants to adopt cutting-edge technology without significant infrastructure investment.
A proof of concept validated the system’s capabilities and potential for full-scale implementation.