使用发声的语音病理检测
Voice Pathology Detection Using Phonation
AI总结：
本研究提出一种基于机器学习的无创框架，利用发声数据的声学特征和RNNs分类检测语音病理，为早期诊断提供自动化工具。
摘要：
语音障碍严重影响交流和生活质量，需要早期准确诊断。喉镜等传统方法具有侵入性、主观性且通常难以普及。本研究提出一种基于机器学习的无创框架，利用发声数据检测语音病理。使用梅尔频率倒谱系数（MFCCs）、色度特征和梅尔频谱图等声学特征，对Saarbrücken Voice Database的发声数据进行分析。采用包括LSTM和注意力机制的循环神经网络（RNNs），将样本分为正常和病理类别。音高平移和高斯噪声添加等数据增强技术提升了模型泛化能力，预处理确保信号质量。Hölder和Hurst指数等基于尺度的特征进一步捕捉信号不规则性和长期依赖。该框架为语音病理早期检测提供无创自动化诊断工具，支持AI驱动的医疗保健并改善患者预后。
英文摘要：
Voice disorders significantly affect communication and quality of life, requiring an early and accurate diagnosis. Traditional methods like laryngoscopy are invasive, subjective, and often inaccessible. This research proposes a noninvasive, machine learning-based framework for detecting voice pathologies using phonation data. Phonation data from the Saarbrücken Voice Database are analyzed using acoustic features such as Mel Frequency Cepstral Coefficients (MFCCs), chroma features, and Mel spectrograms. Recurrent Neural Networks (RNNs), including LSTM and attention mechanisms, classify samples into normal and pathological categories. Data augmentation techniques, including pitch shifting and Gaussian noise addition, enhance model generalizability, while preprocessing ensures signal quality. Scale-based features, such as Hölder and Hurst exponents, further capture signal irregularities and long-term dependencies. The proposed framework offers a noninvasive, automated diagnostic tool for early detection of voice pathologies, supporting AI-driven healthcare, and improving patient outcomes.
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。
