NDLI: Presenting and classification based on three basic speech properties, using Haar wavelet analyzing

Content Provider	IEEE Xplore Digital Library
Author	Sheikhan, M. Safdarkhani, M.K. Gharavian, D.
Copyright Year	2010
Description	Author affiliation: Electrical Engineering Department, Islamic Azad University, South Tehran Branch, Tehran, Iran (Sheikhan, M.; Safdarkhani, M.K.) \|\| Electrical Engineering Department, Power and Water University of Technology, Tehran, Iran (Gharavian, D.)
Abstract	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%.
File Size	274840
File Format	PDF
ISBN	9781424468928
e-ISBN	9781424468935
DOI	10.1109/ICSPS.2010.5555693
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Support vector machines support vector machine feature extraction Feature extraction Speech Wavelet analysis Speech processing Wavelet coefficients
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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