为僧伽罗语读写障碍者打造语音驱动的辅助系统
A Low-Resource Speech-Driven NLP Pipeline for Sinhala Dyslexia Assistance
- 用Whisper转语音为文本，Sinhala-BERT识别错字
- mT5与Mistral模型联合纠错，准确率达70%
- 全流程语音反馈，适合低资源语言无障碍应用
成人读写障碍在非英语语境下研究不足且服务匮乏，尤其对僧伽罗语这类低资源语言。本文针对僧伽罗语成年读写障碍者，构建了一套语音驱动的辅助系统：利用Whisper实现语音转文字，Sinhala-BERT（基于开源BERT微调）识别常见拼写错误，再通过mT5与Mistral联合模型生成修正文本，并使用gTTS将结果转回语音，形成多模态反馈闭环。尽管僧伽罗语数据集有限，系统仍达到0.66的转录准确率、0.7的纠错准确率，整体系统准确率为0.65。结果证明该方法可行有效，凸显了面向弱势语言的包容性NLP技术的重要性。
原文摘要 · Abstract (English)
Dyslexia in adults remains an under-researched and under-served area, particularly in non-English-speaking contexts, despite its significant impact on personal and professional lives. This work addresses that gap by focusing on Sinhala, a low-resource language with limited tools for linguistic accessibility. We present an assistive system explicitly designed for Sinhala-speaking adults with dyslexia. The system integrates Whisper for speech-to-text conversion, SinBERT, an open-sourced fine-tuned BERT model trained for Sinhala to identify common dyslexic errors, and a combined mT5 and Mistral-based model to generate corrected text. Finally, the output is converted back to speech using gTTS, creating a complete multimodal feedback loop. Despite the challenges posed by limited Sinhala-language datasets, the system achieves 0.66 transcription accuracy and 0.7 correction accuracy with 0.65 overall system accuracy. These results demonstrate both the feasibility and effectiveness of the approach. Ultimately, this work highlights the importance of inclusive Natural Language Processing (NLP) technologies in underrepresented languages and showcases a practical
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