Classification of sonorant consonants utilizing empirical mode decomposition
ACSSC（2014）
摘要
In this paper, a method to classify nasal utterance among sonorant consonants utilizing empirical mode decomposition (EMD) is introduced. In this method, each audio signal is divided into overlapping 20 millisecond frames. Then each frame's signal is decomposed by using the EMD. Four different features are extracted from each frame to create a vector. These vectors are employed to train a support vector machine (SVM) with radial basis functions. A different set of audio signals are used to validate the SVM model. The results show an overall correct identification rate of 91.19% for nasals and 89.74% for semivowels.
更多查看译文
关键词
audio signal,speech processing,radial basis function networks,empirical mode decomposition,radial basis functions,nasal utterance classification,emd,support vector machine,nasals,sonorant consonants,semivowels,feature extraction,support vector machine training,identification rate,signal classification,svm model,audio signal processing,frame signal decomposition,sonorant consonant classification,support vector machines
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要