Presenting and Classification Based on Three Basic Speech Properties, Using Haar Wavelet Analyzing
Due to the importance of speech signal in communications, feature extracting and classification of speech based on important attributes of this signal have became a priority. In this paper, a set of extracted speech features is discussed. The language of speech dataset was Farsi with emotional states such as happiness, sadness, interrogative and normal. In this way, three features (i.e. zero crossing rate (ZCR), standard deviation (SD), and average magnitude) are extracted, using Haar wavelet. For this purpose, first the speech signal is divided into five sub-layers, using Haar wavelet and the mentioned features are extracted for each of these sub-bands. Then, the extracted data is classified using support vector machine (SVM) algorithm. In this way, radial basis function (RBF) kernel function is used because of nonlinear relations in data. Also, two methods have been used in classification: one versus of the rest and pair-wise (couple). Empirical results show that the correct classification rate of test data is about 89% when using pair-wise method. For one versus of the rest method, this rate is decreased to 67%.
Wavelet coefficients feature extraction support vector machine
Mansour Sheikhan Mohammad Khadem Safdarkhani Davood Gharavian
Electrical Engineering Department, Islamic Azad University, South Tehran Branch Tehran, Iran Electrical Engineering Department, Power and Water University of Technology Tehran, Iran
国际会议
2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)
大连
英文
1870-1872
2010-07-05(万方平台首次上网日期,不代表论文的发表时间)