IMPROVEMENT NOISE ROBUST SPEAKER IDENTIFICATION: PERFORMANCE IMPROVEMENT REAL TIME SPEAKER IDENTIFICATION SYSTEM UNDER NOISY TALKING CONDITION
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Improvement of Noise Robust Speaker Identification
DE PB NW
ISBN: 9783838364155 bzw. 3838364155, in Deutsch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Von Händler/Antiquariat, BuySomeBooks [52360437], Las Vegas, NV, U.S.A.
Paperback. 88 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
Paperback. 88 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
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IMPROVEMENT OF NOISE ROBUST SPEAKER IDENTIFICATION (2010)
DE PB NW RP
ISBN: 9783838364155 bzw. 3838364155, in Deutsch, Lap Lambert Acad. Publ. Mai 2010, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Titel. - 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.
This item is printed on demand - Print on Demand Titel. - 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.
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IMPROVEMENT OF NOISE ROBUST SPEAKER IDENTIFICATION: PERFORMANCE IMPROVEMENT OF REAL TIME SPEAKER IDENTIFICATION SYSTEM UNDER NOISY TALKING CONDITION
DE PB NW
ISBN: 9783838364155 bzw. 3838364155, in Deutsch, Taschenbuch, neu.
Von Händler/Antiquariat, BuySomeBooks [52360437], Las Vegas, NV, U.S.A.
This item is printed on demand. Paperback. 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. This item ships from La Vergne,TN.
This item is printed on demand. Paperback. 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. This item ships from La Vergne,TN.
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Faculty of Electrical-Computer Engineering RUET Rajshahi Bangladesh.; Md. Fayzur Rahman; Md. Rabiul Islam
Improvement Noise Robust Speaker Identification
EN NW
ISBN: 9783838364155 bzw. 3838364155, in Englisch, neu.
Lieferung aus: Deutschland, Sofort lieferbar.
PERFORMANCE IMPROVEMENT OF REAL TIME SPEAKER IDENTIFICATION SYSTEM UNDER NOISY TALKING CONDITION, 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.
PERFORMANCE IMPROVEMENT OF REAL TIME SPEAKER IDENTIFICATION SYSTEM UNDER NOISY TALKING CONDITION, 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.
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Improvement Noise Robust Speaker Identification (2015)
DE PB NW
ISBN: 9783838364155 bzw. 3838364155, in Deutsch, LAP LAMBERT ACADEMIC PUB 01/05/2015, Taschenbuch, neu.
Von Händler/Antiquariat, Books2Anywhere [190245], Swindon, United Kingdom.
New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. This item is printed on demand.
New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. This item is printed on demand.
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