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This book investigates the development of language identification based on Mel-Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) algorithm using Matlab. In this study, a total of ten speakers were chosen randomly with different languages from online language database. A total of six males and four females were selected as subjects for this research and each of them spoke different languages, including Arabic, Chinese, English, Korean and Malay. The MFCC will be extracted to derive the related feature vector. Vector Quantization (VQ) algorithm is then used as classifier. The recognition rate is then calculated for each language. Several experiments were conducted to find the optimum parameters, in which we found that sampling frequency of 16000 Hz and codebook size of 75 provided good results. On average, the recognition rate for all five languages evaluated was 78%. The experimental results show that our proposed system provides a good recognition rate. This book provided samples of Matlab implementation code for language identification system.
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@Rashida Husain obtained her BEng in Communication Engineering from IIUM, Malaysia in 2017. She received best FYP-IEEE WIE. @Teddy Surya Gunawan received his PhD from UNSW, Australia in 2007 and is currently an Associate Professor at IIUM. @Mira Kartiwi obtained her PhD from UOW, Australia in 2009 and is currently an Associate Professor at IIUM.
