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

TL;DR

This paper compares three voice command pipelines for drone control, including traditional speech recognition with LLM, direct classification, and Siamese networks, evaluating their performance and suitability for intuitive human-drone interaction.

Contribution

It introduces and empirically evaluates three novel voice command pipelines for drone control, highlighting their respective advantages and limitations.

Findings

Siamese network approach offers faster inference times.

Direct mapping pipeline achieves higher accuracy.

Traditional STT + LLM pipeline provides greater flexibility.

Abstract

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.