标题： 一种多语言框架用于构音障碍：检测、严重程度分类、语音到文本和清晰语音生成 显示英文标题

标题： A Multilingual Framework for Dysarthria: Detection, Severity Classification, Speech-to-Text, and Clean Speech Generation

摘要： 构音障碍是一种运动性言语障碍，导致言语缓慢且常常难以理解。 言语可理解性显著影响交流，导致社交互动中的障碍。 构音障碍通常是神经系统疾病的特征，包括帕金森病和ALS，但目前的工具在不同语言和严重程度之间缺乏通用性。 在本研究中，我们提出了一种统一的基于人工智能的多语言框架，解决了六个关键组件：(1) 二元构音障碍检测，(2) 严重程度分类，(3) 清晰言语生成，(4) 言语到文本转换，(5) 情绪检测，和(6) 语音克隆。 我们分析了英语、俄语和德语的数据集，使用基于频谱图的可视化和声学特征提取来指导模型训练。 我们的二元检测模型在所有三种语言中达到了97%的准确率，证明了在不同语言之间的强大泛化能力。 严重程度分类模型也达到了97%的测试准确率，可解释的结果显示模型注意力集中在低谐波上。 我们的翻译管道，在配对的俄语构音障碍和清晰语音上进行训练，以低训练（0.03）和测试（0.06）L1损失重建了可理解的输出。 鉴于英语构音障碍-清晰语音对的可用性有限，我们在英语数据上微调了俄语模型，并实现了改进的损失值0.02（训练）和0.03（测试），突显了跨语言迁移学习在低资源环境中的潜力。 我们的言语到文本管道在三个周期后实现了0.1367的词错误率，表明对构音障碍言语的准确转录，并从转录的言语中实现了下游情绪识别和语音克隆。 总体而言，本研究的结果和产品可用于诊断构音障碍，并改善不同语言患者的交流和理解。 显示英文摘要

摘要： Dysarthria is a motor speech disorder that results in slow and often incomprehensible speech. Speech intelligibility significantly impacts communication, leading to barriers in social interactions. Dysarthria is often a characteristic of neurological diseases including Parkinson's and ALS, yet current tools lack generalizability across languages and levels of severity. In this study, we present a unified AI-based multilingual framework that addresses six key components: (1) binary dysarthria detection, (2) severity classification, (3) clean speech generation, (4) speech-to-text conversion, (5) emotion detection, and (6) voice cloning. We analyze datasets in English, Russian, and German, using spectrogram-based visualizations and acoustic feature extraction to inform model training. Our binary detection model achieved 97% accuracy across all three languages, demonstrating strong generalization across languages. The severity classification model also reached 97% test accuracy, with interpretable results showing model attention focused on lower harmonics. Our translation pipeline, trained on paired Russian dysarthric and clean speech, reconstructed intelligible outputs with low training (0.03) and test (0.06) L1 losses. Given the limited availability of English dysarthric-clean pairs, we fine-tuned the Russian model on English data and achieved improved losses of 0.02 (train) and 0.03 (test), highlighting the promise of cross-lingual transfer learning for low-resource settings. Our speech-to-text pipeline achieved a Word Error Rate of 0.1367 after three epochs, indicating accurate transcription on dysarthric speech and enabling downstream emotion recognition and voice cloning from transcribed speech. Overall, the results and products of this study can be used to diagnose dysarthria and improve communication and understanding for patients across different languages.