Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/10776
Title: An application of speech recognition with support vector machines
Authors: Eray, O.
Tokat, Sezai
İplikci, Serdar
Keywords: machine learning
speech coding
speech recognition
support vector machines
Codes (symbols)
Computer crime
Digital forensics
Electronic crime countermeasures
Learning systems
Speech coding
Speech communication
Support vector machines
Human-machine communication
Pre-estimation
Speaker recognition
Speaker verification
Speech recognition systems
SVM classifiers
Training and testing
Training phase
Speech recognition
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Speech recognition systems aim to make human-machine communication quickly and easily. In recent years, various researches and studies have been carried out to develop speech recognition systems. Examples of these studies are speech recognition, speaker recognition and speaker verification. In this study, speech recognition systems were investigated, methods used in the literature were investigated and a Turkish speech recognition application was developed. The application consists of speech coding and speech recognition. Firstly 20 Turkish words which are frequently used on the computer were determined. There are 20 records from each word. A total of 400 words were recorded on the computer with a microphone. In the speech coding section of the application, these words recorded on the computer are encoded by the Linear Pre-estimation Coding (LPC) method and the LPC parameters for each word are obtained. In the speech recognition section of the application, the Support Vector Machines (SVM) method is used. Two types of SVM classifiers are designed. These are the Soft Margin SVM (SM-SVM) classifier and the Least Square SVM (LS-SVM) classifier. Classification consists of training and testing stages. Of the 400 coded words, 200 were used for the training phase and 200 were used for the testing phase. As a result, 91% accurate recognition success for the SM-SVM classifier; 71% correct recognition of the LS-SVM classifier has been achieved. © 2018 IEEE.
URI: https://hdl.handle.net/11499/10776
https://doi.org/10.1109/ISDFS.2018.8355321
ISBN: 9781538634493
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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