DocumentCode
2313958
Title
Text-Independent Speaker Identification Using Hidden Markov Models
Author
Deshpande, Mangesh S. ; Holambe, Raghunath S.
Author_Institution
SRES Coll. of Eng., Kopargaon
fYear
2008
fDate
16-18 July 2008
Firstpage
641
Lastpage
644
Abstract
This paper presents a closed-set, text-independent speaker identification using continuous density hidden Markov model (CDHMM). Each registered speaker has a separate HMM which is trained using Baum-Welch algorithm. The system performance has been studied for different system parameters such as the number of states, number of mixture components per state and the amount of data required for training. Identification accuracy of 100% is achieved by conducting the experiments on TIMIT database.
Keywords
hidden Markov models; speaker recognition; Baum-Welch algorithm; CDHMM; TIMIT database; continuous density hidden Markov model; hidden Markov models; text-independent speaker identification; Concatenated codes; Databases; Educational institutions; Hidden Markov models; Probability density function; Speaker recognition; Speech; Strontium; System performance; Vector quantization; Speaker identification; admissible wavelet packet tree; hidden Markov model;
fLanguage
English
Publisher
ieee
Conference_Titel
Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on
Conference_Location
Nagpur, Maharashtra
Print_ISBN
978-0-7695-3267-7
Electronic_ISBN
978-0-7695-3267-7
Type
conf
DOI
10.1109/ICETET.2008.46
Filename
4579978
Link To Document