Paper ID: 2407.08658

Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks

Lucca Emmanuel Pineli Simões, Lucas Brandão Rodrigues, Rafaela Mota Silva, Gustavo Rodrigues da Silva

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.

Submitted: Jul 10, 2024