...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of speech technology >Multitaper perceptual linear prediction features of voice samples to discriminate healthy persons from early stage Parkinson diseased persons
【24h】

Multitaper perceptual linear prediction features of voice samples to discriminate healthy persons from early stage Parkinson diseased persons

机译：语音样本的多锥感知线性预测特征​​可将健康人与早期帕金森病患者区分开

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The performance of multitaper perceptual linear prediction (PLP) features of speech samples to discriminate healthy and early stage Parkinson diseased subjects is investigated in this paper. The PLP features are conventionally obtained by computing the power spectrum using a single tapered Hamming window. This estimated spectrum exhibits large variance which can be reduced by computing the weighted average of power spectra obtained using a set of tapered windows, leading to multitaper spectral estimation. In this investigation, two multitaper techniques namely Sine wave taper and Thomson multitaper along with the conventional single taper windowing are investigated. Artificial Neural network is then used to classify the PLP features extracted by applying the three types of window tapers on the speech signals of healthy and early stage Parkinson affected people and their respective performances are compared. The results show more accuracy using the multitaper techniques when compared with the conventional single taper technique. It is seen that the accuracy obtained using Sine wave tapers as well as Thomson multitaper is maximum for five tapers. An improvement in the recognition accuracy by 7.5% using the Sine tapers and by 6.9% using the Thomson tapers is obtained when compared with the conventional method. An improvement in other performance measures like Equal error rate, False positive rate, False negative rate, Sensitivity and Specificity is also observed in the multitaper techniques.
机译：本文研究了语音样本的多锥感知线性预测（PLP）功能对健康和早期帕金森病患者的区分能力。通常通过使用单个锥形汉明窗计算功率谱来获得PLP特征。该估算频谱显示出较大的方差，可以通过计算使用一组渐缩窗获得的功率谱的加权平均值来减小此方差，从而实现多锥谱估计。在这项研究中，研究了两种多锥度技术，即正弦波锥度和Thomson多锥度，以及常规的单锥度加窗。然后使用人工神经网络对在健康和早期帕金森病患者的语音信号上应用三种类型的窗口锥度提取的PLP特征进行分类，并对它们各自的性能进行比较。与常规的单锥度技术相比，使用多锥度技术的结果显示出更高的准确性。可以看出，使用正弦波锥度和Thomson多锥度获得的精度对于5个锥度是最大的。与传统方法相比，使用正弦锥度可将识别准确度提高7.5％，使用汤姆逊锥度可将识别准确度提高6.9％。在多锥技术中还观察到其他性能指标的改善，例如均等错误率，误报率，误报率，灵敏度和特异性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号