卷积神经网络（CNN）和循环神经网络（RNN）在语音病理学检测中的比较分析。
Comparative Analysis of CNN and RNN for Voice Pathology Detection.
机构信息
Department of Biomedical Engineering and Department of Electrical Engineering, Ziauddin University Faculty of Engineering Science, Technology, and Management, Karachi, Pakistan.
Department of Electrical Engineering and Department of Software Engineering, Ziauddin University Faculty of Engineering Science, Technology, and Management, Karachi, Pakistan.
出版信息
Biomed Res Int. 2021 Apr 14;2021:6635964. doi: 10.1155/2021/6635964. eCollection 2021.
Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.
基于计算机声学检查的诊断可能在早期诊断以及监测甚至改善有效的病理性语音诊断中发挥非常重要的作用。各种声学指标测试声音的健康状况。这些参数的精度也与语音噪声检测算法有关。其理念是从声音中检测出疾病病理。首先，我们在奇异值分解数据集上应用特征提取。在特征提取之后，系统输入进入 27 个神经元层的卷积和递归神经网络。我们将数据集分为训练和测试，在 10k 折验证后，CNN 和 RNN 的报告准确率分别为 87.11%和 86.52%。使用 10 折交叉验证来评估分类器的性能。在具有一个 NVIDIA Titan X GPU 的 Linux 工作站上，使用 TensorFlow 包在 Python 中编写了程序代码。
Zotero 插件