IIUM Repository

Development of language identification system using MFCC and vector quantization

Gunawan, Teddy Surya and Husain, Rashida and Kartiwi, Mira (2017) Development of language identification system using MFCC and vector quantization. In: 4th IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) 2017, 28th-30th November 2017, Putrajaya. (Unpublished)

[img] PDF
Restricted to Registered users only

Download (2MB) | Request a copy


This paper investigates the development of language identification based on Mel-Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) algorithm. 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.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 5588/60070
Uncontrolled Keywords: language identification; MFCC; VQ; recognition rate
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Prof. Dr. Teddy Surya Gunawan
Date Deposited: 14 Dec 2017 14:43
Last Modified: 14 Dec 2017 14:43
URI: http://irep.iium.edu.my/id/eprint/60070

Actions (login required)

View Item View Item


Downloads per month over past year