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At present Automatic Speaker Recognition system is a very important issue due to its diverse applications. Hence, it becomes absolutely necessary to obtain models that take into consideration the speaking style of a person, vocal tract information, timbral qualities of his voice and other congenital information regarding his voice. The study of Bengali speech recognition and speaker identification is scarce in the literature. Hence the need arises for involving Bengali subjects in modelling our speaker identification engine. Being subdivided into many dialects, the language Bengali has different phraseologies corresponding to particular regions. Thus we selected subjects from different regions of Bengal for our research. Different dialects are expected to reflect much contrasting acoustic features. A pool of 10 participants have been selected for this study who are comfortable with different dialects, and a total of 8 Bengali speech database was recorded from each of them. The text database each of about 3 min in length have been selected from famous Bengali writings and are supposed to convey a meaning even in the speech emotional perspective. In this work, we have extracted some acoustic features of speech using non linear multifractal analysis. The multifractal spectral width reveals essentially the complexity associated with the speech signals taken. The source characteristics have been quantified with the help of different classifiers like Correlation Matrix, skewness of multifractal curve etc. The Results obtained from this study gives a good recognition rate for Bengali Speakers.
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