论文标题
深度神经网络自动评估烦躁不安
Deep Neural Network for Automatic Assessment of Dysphonia
论文作者
论文摘要
这项工作的目的是为人声质量领域的深度神经网络的理解和改善做出贡献。获得了一个预测GRBAS量表总体严重程度的感知评估的神经网络。该设计着重于振幅摄动，频率扰动和噪声。将结果与人类评估者对相同数据的表现进行比较。神经网络的精度和平均绝对误差都接近人类评估者的表现，超过了评估者间的表现。
The purpose of this work is to contribute to the understanding and improvement of deep neural networks in the field of vocal quality. A neural network that predicts the perceptual assessment of overall severity of dysphonia in GRBAS scale is obtained. The design focuses on amplitude perturbations, frequency perturbations, and noise. Results are compared with performance of human raters on the same data. Both the precision and the mean absolute error of the neural network are close to human intra-rater performance, exceeding inter-rater performance.
