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arXiv · 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
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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.

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Lucca Emmanuel Pineli Simões, Lucas Brandão Rodrigues, Rafaela Mota Silva, Gustavo Rodrigues da Silva. 2024-07-10. Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks. https://arxiv.org/abs/2407.08658

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