JPR_V3_N1_RP1 Vinay Kumar Jain Neeta Tripathi Journal on Pattern Recognition 2350-112X 3 1 1 6 MFCC, Delta Coefficients, Multi-language, Impact of Language The speaker identification systems work only in a single language environment using sufficient data. Many countries including India are multilingual and hence the effect of multiple languages on a speaker identification system needs to be investigated. Speaker identification system shows poor performance when training is done in one language and the testing in another language. This is a major problem in multilingual speaker identification system. The main objective of this research work is to observe the impact of the languages on multilingual speaker identification system and identifying the variation of MFCC feature vector values in multilingual environments, which will help to design multilingual speaker identification system. The present paper explores the experimental result carried out on collected database of multilingual speakers of three Indian languages. The speech database consists of speech data recorded from 100 speakers including male and female. The Mel Frequency Cepstral Coefficients (MFCC) as a front end feature vectors are extracted from the speech signals. The minimum, maximum and mean values of the feature vectors have been calculated for the analysis. It is observed that Rajasthani language has the larger values as compared to Hindi language and Marathi Language in minimum values of the feature vectors, where as Marathi Language has the larger values as compared to Hindi language and Rajasthani language in maximum values of feature vectors. The impact of the languages on multilingual speaker identification system has been evaluated. March - May 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=8102