  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.
