希尔伯特-黄变换
计算机科学
语音处理
语音识别
时频分析
瞬时相位
Mel倒谱
人工智能
短时傅里叶变换
熵(时间箭头)
信号处理
模式识别(心理学)
特征提取
傅里叶变换
数学
傅里叶分析
数字信号处理
计算机硬件
滤波器(信号处理)
物理
数学分析
电信
量子力学
计算机视觉
雷达
作者
Pankaj Warule,Siba Prasad Mishra,Suman Deb,Deepak Joshi
标识
DOI:10.1109/tencon58879.2023.10322409
摘要
The current advancements in machine learning research pertaining to speech and health are highly interesting. One aspect of speech-processing research that is gaining popularity is the use of computational paralinguistic analysis to evaluate a variety of health conditions. In this study, we have used the Hilbert-Huang transform (HHT) for the time-frequency analysis of speech signals for the identification of the common cold. The HHT is a time-frequency transform that is adaptive and ideal for non-linear and non-stationary signals. The HHT is a combination of empirical mode decomposition (EMD) and the Hilbert transform (HT). The HHT gives the time-frequency representation (TFR) matrix of the speech signal. Then, the entropy of each frequency component in TFR is computed and used as a distinguishing feature between cold and healthy speech. The efficacy of the proposed methodology is evaluated on the URTIC dataset using a deep neural network. The proposed features achieve UARs of 65.66% and 65.26%, respectively, on the develop and test partitions. The results of the study demonstrate that the time-frequency entropy features extracted using the HHT are effective in distinguishing between cold and healthy speech.
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