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

arXiv: https://arxiv.org/abs/2407.08658 · HTML: https://mlsanity.com/paper/2407.08658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition](https://mlsanity.com/paper/2009.04215/) (2020)

## Built on by (most cited first, 2 of 2)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Normalise or condition? Noise-floor front-ends for on-board keyword spotting under UAV rotor ego-noise](https://mlsanity.com/paper/2609.31699/) (2026)

## Abstract (arXiv)

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.