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Automatic identification of Vietnamese dialects

The experiment result for the dialect corpus of Vietnamese shows that the performance of dialectal identification with baseline increases from 58.6% for the case using only MFCC coefficients to 70.8% for the case using MFCC coefficients and the information of fundamental frequency. By combining the formants and their bandwidths with the normalized F0 according to average and standard deviation F0, the best recognition rate is 72.2%. | Journal of Computer Science and Cybernetics, V.32, N.1 (2016), 18–29 DOI: 10.15625/1813-9663/32/1/7905 AUTOMATIC IDENTIFICATION OF VIETNAMESE DIALECTS PHAM NGOC HUNG1,2 , TRINH VAN LOAN1,2 , NGUYEN HONG QUANG2 1 Faculty of Information Technology, Hung Yen University of Technology and Education, of Information and Communication Technology, Hanoi University of Science and Technology 1,2 pnhung@utehy.edu.vn; 1,2 loantv@soict.hust.edu.vn; 2 quangnh@soict.hust.edu.vn 2 School Abstract. The dialect identification has been under study for many languages over the world nevertheless the research on signal processing for Vietnamese dialects is still limited and there are not many published works. There are many different dialects for Vietnamese. The influence of dialectal features on speech recognition systems is important. If the information about dialects is known during speech recognition process, the performance of recognition systems will be better because the corpus of these systems is normally organized according to different dialects. In our experiments, MFCC coefficients, formants, correspondent bandwidths and the fundamental frequency with its variants are input parameters for GMM. The experiment result for the dialect corpus of Vietnamese shows that the performance of dialectal identification with baseline increases from 58.6% for the case using only MFCC coefficients to 70.8% for the case using MFCC coefficients and the information of fundamental frequency. By combining the formants and their bandwidths with the normalized F 0 according to average and standard deviation F 0, the best recognition rate is 72.2%. Keywords. Fundamental frequency, MFCC, Formant, Bandwidth, GMM, Vietnamese dialects, identification. 1. INTRODUCTION Vietnamese is a tonal language with many different dialects. It is the diversity of Vietnamese dialects that remains a great challenge to the systems of Vietnamese recognition. In other words, the pronunciation modality of the word .