Open AccessKurdish Spoken Letter Recognition based on k-NN and SVM Model
Author(s) -
Zrar Khalid Abdul
Publication year - 2020
Publication title -
govarî zankoy ṛapeṛîn
Language(s) - English
Resource type - Journals
eISSN - 2522-7130
pISSN - 2410-1036
DOI - 10.26750/vol(7).no(4).paper1
Subject(s) - mel frequency cepstrum , computer science , speech recognition , support vector machine , artificial intelligence , pattern recognition (psychology) , linear predictive coding , coding (social sciences) , feature extraction , linear prediction , cepstrum , speech coding , mathematics , statistics
Automatic recognition of spoken letters is one of the most challenging tasks in the area of speech recognition system. In this paper, different machine learning approaches are used to classify the Kurdish alphabets such as SVM and k-NN where both approaches are fed by two different features, Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCCs). Moreover, the features are combined together to learn the classifiers. The experiments are evaluated on the dataset that are collected by the authors as there as not standard Kurdish dataset. The dataset consists of 2720 samples as a total. The results show that the MFCC features outperforms the LPC features as the MFCCs have more relative information of vocal track. Furthermore, fusion of the features (MFCC and LPC) is not capable to improve the classification rate significantly.