光谱图
计算机科学
语音识别
人工智能
特征(语言学)
短时傅里叶变换
模式识别(心理学)
卷积神经网络
傅里叶变换
数学
语言学
数学分析
哲学
傅里叶分析
作者
Lei Geng,Hongfeng Shan,Zhitao Xiao,Wei Wang,Mei Wei
出处
期刊:Biomedizinische Technik
[De Gruyter]
日期:2021-11-29
卷期号:66 (6): 613-625
被引量:2
标识
DOI:10.1515/bmt-2021-0112
摘要
Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to enhance the signal's harmonics and denoise. Second, a pre-trained convolutional neural network (CNN) is used as the backbone network to extract sound state features and vocal cord vibration features from the two signals. To obtain a better classification effect, the fused features are input into the long short-term memory (LSTM) network for voice feature selection and enhancement. The proposed system achieves 95.73% for accuracy with 96.10% F1-score and 96.73% recall using the Saarbrucken Voice Database (SVD); thus, enabling a new method for pathological speech detection.
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