Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks
<|reference_start|>Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks: 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.<|reference_end|>
@article{simões2024evaluating,
    title={Evaluating Voice Command Pipelines for Drone Control: From STT and LLM
  to Direct Classification and Siamese Networks},
    author={Lucca Emmanuel Pineli Sim~oes, Lucas Brand~ao Rodrigues, Rafaela
  Mota Silva, Gustavo Rodrigues da Silva},
    journal={arXiv preprint arXiv:2407.08658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08658},
    primaryClass={cs.SD cs.AI}
}