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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08658 |
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| _version_ | 1866914866582781952 |
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| author | Simões, Lucca Emmanuel Pineli Rodrigues, Lucas Brandão Silva, Rafaela Mota da Silva, Gustavo Rodrigues |
| author_facet | Simões, Lucca Emmanuel Pineli Rodrigues, Lucas Brandão Silva, Rafaela Mota da Silva, Gustavo Rodrigues |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08658 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks Simões, Lucca Emmanuel Pineli Rodrigues, Lucas Brandão Silva, Rafaela Mota da Silva, Gustavo Rodrigues Sound Artificial Intelligence I.2.7; I.2.10 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. |
| title | Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks |
| topic | Sound Artificial Intelligence I.2.7; I.2.10 |
| url | https://arxiv.org/abs/2407.08658 |