论文标题
比较基于GMM-UBM和I-QUENTOR的用户模型的语音，手写和步态评估帕金森氏病患者的比较
Comparison of user models based on GMM-UBM and i-vectors for speech, handwriting, and gait assessment of Parkinson's disease patients
论文作者
论文摘要
帕金森氏病是一种神经退行性疾病，其特征是存在不同的运动障碍。已经考虑了来自语音，手写和步态信号的信息来评估患者的神经系统状态。另一方面，基于高斯混合模型的用户模型 - 通用背景模型（GMM-UBM）和I-向量被认为是诸如扬声器验证之类的生物识别应用程序中的最新技术，因为它们能够对特定的扬声器特定性状进行建模。这项研究介绍了使用语音，手写和步态中的信息来评估帕金森氏病患者的神经系统状态。结果表明，在评估患者神经系统状态时，每种信号的不同特征集的重要性。
Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMM-UBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Parkinson's patients using information from speech, handwriting, and gait. The results show the importance of different feature sets from each type of signal in the assessment of the neurological state of the patients.
