Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks
This work addresses the problem of enhancing human-machine interaction for drone users, but it is incremental as it compares existing techniques without introducing a fundamentally new approach.
This paper tackled the problem of enabling intuitive voice control for drones by developing and comparing three voice command pipelines, including STT+LLM, direct classification, and Siamese networks, with results showing variations in inference time, accuracy, efficiency, and flexibility.
This paper presents the development and comparative evaluation of three voice command pipelines for controlling a Tello drone, using speech recognition and deep learning techniques. The aim is to enhance human-machine interaction by enabling intuitive voice control of drone actions. The pipelines developed include: (1) a traditional Speech-to-Text (STT) followed by a Large Language Model (LLM) approach, (2) a direct voice-to-function mapping model, and (3) a Siamese neural network-based system. Each pipeline was evaluated based on inference time, accuracy, efficiency, and flexibility. Detailed methodologies, dataset preparation, and evaluation metrics are provided, offering a comprehensive analysis of each pipeline's strengths and applicability across different scenarios.