用开源技术与AI打造无障碍教育平台，助力特殊需求学生平等学习。
Development of an Inclusive Educational Platform Using Open Technologies and Machine Learning: A Case Study on Accessibility Enhancement
- 融合机器学习与自然语言处理，实现语音输入转文字、实时物体识别。
- 语音合成准确率高，跨平台应用支持离线运行，用户体验良好。
- 适合教育科技开发者、特殊教育研究者及政策制定者参考。
本研究针对特殊需求学生教育包容性难题，提出并开发了一款集成机器学习、自然语言处理与跨平台界面的包容性教育平台。平台具备语音识别功能，支持语音指令与语音输入转文字；基于YOLOv5模型实现实时物体识别，适配教育场景；采用带注意力机制的seq2seq模型实现图素到音素（G2P）转换，提升文本转语音的自然流畅度；并使用Flutter开发跨平台移动端应用，通过TensorFlow Lite实现设备端推理。实验结果表明，该平台在教育场景中表现出高准确率、良好可用性及积极影响，验证了其作为教育包容性工具的有效性。该项目凸显了开放可访问技术在推动包容性优质教育中的重要性。
原文摘要 · Abstract (English)
This study addresses the pressing challenge of educational inclusion for students with special needs by proposing and developing an inclusive educational platform. Integrating machine learning, natural language processing, and cross-platform interfaces, the platform features key functionalities such as speech recognition functionality to support voice commands and text generation via voice input; real-time object recognition using the YOLOv5 model, adapted for educational environments; Grapheme-to-Phoneme (G2P) conversion for Text-to-Speech systems using seq2seq models with attention, ensuring natural and fluent voice synthesis; and the development of a cross-platform mobile application in Flutter with on-device inference execution using TensorFlow Lite. The results demonstrated high accuracy, usability, and positive impact in educational scenarios, validating the proposal as an effective tool for educational inclusion. This project underscores the importance of open and accessible technologies in promoting inclusive and quality education.
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