特征提取
计算机科学
Mel倒谱
小波变换
语音识别
特征(语言学)
人工智能
语音活动检测
背景(考古学)
语音处理
信号处理
统计分类
模式识别(心理学)
倒谱
小波
数字信号处理
哲学
古生物学
生物
语言学
计算机硬件
作者
C. Okan Şakar,Görkem Serbes,Ayşegül Gündüz,Hünkar Can Tunç,Hatice Nizam Ozogur,Betül Erdoğdu Şakar,Melih Tütüncü,Tarkan Aydın,M. Erdem Isenkul,Hülya Apaydın
标识
DOI:10.1016/j.asoc.2018.10.022
摘要
In recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson’s disease (PD) based on measuring the motor system disorders caused by the disease. As approximately 90% percent of PD patients exhibit some form of vocal disorders in the earlier stages of the disease, the recent PD telediagnosis studies focus on the detection of the vocal impairments from sustained vowel phonations or running speech of the subjects. In these studies, various speech signal processing algorithms have been used to extract clinically useful information for PD assessment, and the calculated features were fed to learning algorithms to construct reliable decision support systems. In this study, we apply, to the best of our knowledge for the first time, the tunable Q-factor wavelet transform (TQWT) to the voice signals of PD patients for feature extraction, which has higher frequency resolution than the classical discrete wavelet transform. We compare the effectiveness of TQWT with the state-of-the-art feature extraction methods used in diagnosis of PD from vocal disorders. For this purpose, we have collected the voice recordings of 252 subjects in the context of this study and extracted multiple feature subsets from the voice recordings. The feature subsets are fed to multiple classifiers and the predictions of the classifiers are combined with ensemble learning approaches. The results show that TQWT performs better or comparable to the state-of-the-art speech signal processing techniques used in PD classification. We also find that Mel-frequency cepstral and the tunable-Q wavelet coefficients, which give the highest accuracies, contain complementary information in PD classification problem resulting in an improved system when combined using a filter feature selection technique.
科研通智能强力驱动
Strongly Powered by AbleSci AI