无人机控制语音命令流水线评估：从STT、LLM到直接分类与Siamese网络
Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks
AI总结：
本文针对无人机语音控制需求，开发并对比了STT+LLM、直接语音到功能映射、Siamese网络三种语音命令流水线，从多维度评估其性能，为不同场景下的无人机语音控制方案选择提供参考。
摘要：
本文介绍了三种用于控制Tello无人机的语音命令流水线的开发与对比评估，采用语音识别与深度学习技术，旨在通过实现无人机动作的直观语音控制来增强人机交互。所开发的流水线包括：（1）传统语音转文本（Speech-to-Text, STT）后接大语言模型（Large Language Model, LLM）的方法，（2）直接语音到功能映射模型，（3）基于Siamese神经网络的系统。研究从推理时间、准确率、效率和灵活性四个维度评估了各流水线，同时给出了详细的方法流程、数据集准备过程与评估指标，全面分析了各流水线的优势及在不同场景下的适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
