In this book, an improved strategy to the improvement of an automated text dependent speaker identification system as a biometrically-based technology has been studied and it is concerned with the close set text dependent speaker identification process using genetically optimized Hidden Markov Model with cepstral based features. At first, speech is taken by using microphone. Then some speech pre-processing techniques such as start and end points detection, silence part removal, pre-emphasis filtering, speech segmentation, windowing etc techniques have been applied. After pre-processing, features are extracted by different techniques to optimize the performance of the identification. RCC, MFCC, ?MFCC, ??MFCC, LPC and LPCC have been used to extract the features. To design the codebook, Genetic Algorithm has been used. Finally, HMM is used in the learning and identification phases. To remove the background noise, Wiener filter has been used. To measure the performance, a standard speech corpus NOIZEUS has been used. The experimental result shows the superiority of this proposed GA-HMM based close-set real time speaker identification system.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, an improved strategy to the improvement of an automated text dependent speaker identification system as a biometrically-based technology has been studied and it is concerned with the close set text dependent speaker identification process using genetically optimized Hidden Markov Model with cepstral based features. At first, speech is taken by using microphone. Then some speech pre-processing techniques such as start and end points detection, silence part removal, pre-emphasis filtering, speech segmentation, windowing etc techniques have been applied. After pre-processing, features are extracted by different techniques to optimize the performance of the identification. RCC, MFCC, ¿MFCC, ¿¿MFCC, LPC and LPCC have been used to extract the features. To design the codebook, Genetic Algorithm has been used. Finally, HMM is used in the learning and identification phases. To remove the background noise, Wiener filter has been used. To measure the performance, a standard speech corpus NOIZEUS has been used. The experimental result shows the superiority of this proposed GA-HMM based close-set real time speaker identification system. 88 pp. Englisch. N° de réf. du vendeur 9783838364155
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Islam Md. RabiulMd. Rabiul Islam is an Assistant Professor in the Department of Computer Science & Engineering at Rajshahi University of Engineering & Technology. His research interests include bio-informatics, human-computer interac. N° de réf. du vendeur 5416756
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book, an improved strategy to the improvement of an automated text dependent speaker identification system as a biometrically-based technology has been studied and it is concerned with the close set text dependent speaker identification process using genetically optimized Hidden Markov Model with cepstral based features. At first, speech is taken by using microphone. Then some speech pre-processing techniques such as start and end points detection, silence part removal, pre-emphasis filtering, speech segmentation, windowing etc techniques have been applied. After pre-processing, features are extracted by different techniques to optimize the performance of the identification. RCC, MFCC, ¿MFCC, ¿¿MFCC, LPC and LPCC have been used to extract the features. To design the codebook, Genetic Algorithm has been used. Finally, HMM is used in the learning and identification phases. To remove the background noise, Wiener filter has been used. To measure the performance, a standard speech corpus NOIZEUS has been used. The experimental result shows the superiority of this proposed GA-HMM based close-set real time speaker identification system.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch. N° de réf. du vendeur 9783838364155
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, an improved strategy to the improvement of an automated text dependent speaker identification system as a biometrically-based technology has been studied and it is concerned with the close set text dependent speaker identification process using genetically optimized Hidden Markov Model with cepstral based features. At first, speech is taken by using microphone. Then some speech pre-processing techniques such as start and end points detection, silence part removal, pre-emphasis filtering, speech segmentation, windowing etc techniques have been applied. After pre-processing, features are extracted by different techniques to optimize the performance of the identification. RCC, MFCC, ¿MFCC, ¿¿MFCC, LPC and LPCC have been used to extract the features. To design the codebook, Genetic Algorithm has been used. Finally, HMM is used in the learning and identification phases. To remove the background noise, Wiener filter has been used. To measure the performance, a standard speech corpus NOIZEUS has been used. The experimental result shows the superiority of this proposed GA-HMM based close-set real time speaker identification system. N° de réf. du vendeur 9783838364155
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. IMPROVEMENT OF NOISE ROBUST SPEAKER IDENTIFICATION | PERFORMANCE IMPROVEMENT OF REAL TIME SPEAKER IDENTIFICATION SYSTEM UNDER NOISY TALKING CONDITION | Md. Rabiul Islam (u. a.) | Taschenbuch | 88 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838364155 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 101106111
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