IMPROVEMENT OF THE TEXT DEPENDENT SPEAKER IDENTIFICATION SYSTEM USING DISCRETE MMM WITH CEPSTRAL BASED FEATURES

Islam, Md. Rabiul; Rahman, Md. Fayzur; Khan, 3Muhammad Abdul Goffar

dc.contributor.author	Islam, Md. Rabiul
dc.contributor.author	Rahman, Md. Fayzur
dc.contributor.author	Khan, 3Muhammad Abdul Goffar
dc.date.accessioned	2012-11-10T09:58:40Z
dc.date.accessioned	2019-05-29T05:04:04Z
dc.date.available	2012-11-10T09:58:40Z
dc.date.available	2019-05-29T05:04:04Z
dc.date.issued	2011-07-01
dc.identifier.uri	http://hdl.handle.net/20.500.11948/543
dc.description.abstract	In this paper, an improved strategy for automated text based speaker identification scheme has been proposed. The identification process incorporates the Hidden Markov Model technique. After preprocessing the speech, HMM is used in the learning and identification. Features are extracted by different techniques such as RCC, MFCC, ÄMFCC, ÄÄMFCC, LPC and LPCC which is almost different in each case. The highest identification rate of 93% has been achieved in the close set text dependent speaker identification system.	en_US
dc.language.iso	en	en_US
dc.publisher	Daffodil International University	en_US
dc.subject	Biometric Technologies, Automatic Speaker Identification, Cepstral Coefficients, Feature Extraction, Hidden Markov Model.	en_US
dc.title	IMPROVEMENT OF THE TEXT DEPENDENT SPEAKER IDENTIFICATION SYSTEM USING DISCRETE MMM WITH CEPSTRAL BASED FEATURES	en_US
dc.type	Article	en_US