alaa refeis (9 resultados)

Condición: New. pp. 132.

Taschenbuch. Condición: Neu. FPGA Implementation of Speech Recognition System Based on HMM | Alaa Refeis (u. a.) | Taschenbuch | 132 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783847346029 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduced an approach to design and implement an embedded SoPC (System on Programmable Chip) technique with Altera Nios II processor on a FPGA chip for real-time speech recognition system by developing hardware/software with minimum usage of resources (hardware components) and relatively small size software. This reduces the memory utilization, achieved by using Mel Frequency Cepstral Coefficients (MFCCs) technique as feature extraction combined with its first derivative ( MFCCs) including power computation of the speech frames (i.e. E,MFCC, E,and MFCC), called observation vector of the speech signal. To model the obtained observation, Gaussian Mixture Model (GMM) has been used, which is passed to a Hidden Markov Model (HMM) as probabilistic model to process the GMM statistically to take a decision on the uttered words recognition, whether a single or composite, one or more syllable words (i.e. one, six, welcome). The words that are used for training and testing the system included selected English and Arabic words. 132 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Refeis AlaaB.Sc. Electronics and Communication Engineering.Higher Diploma Computer Science / Artificial Intelligence.M.Sc. Electronics Engineering.GSM mobile communication.Computer Networking/Administration.Digital System Design.Embe.

Condición: New. Print on Demand pp. 132 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 132.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduced an approach to design and implement an embedded SoPC (System on Programmable Chip) technique with Altera Nios II processor on a FPGA chip for real-time speech recognition system by developing hardware/software with minimum usage of resources (hardware components) and relatively small size software. This reduces the memory utilization, achieved by using Mel Frequency Cepstral Coefficients (MFCCs) technique as feature extraction combined with its first derivative (¿MFCCs) including power computation of the speech frames (i.e. E,MFCC,¿E,and ¿MFCC), called observation vector of the speech signal. To model the obtained observation, Gaussian Mixture Model (GMM) has been used, which is passed to a Hidden Markov Model (HMM) as probabilistic model to process the GMM statistically to take a decision on the uttered words recognition, whether a single or composite, one or more syllable words (i.e. one, six, welcome). The words that are used for training and testing the system included selected English and Arabic words.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduced an approach to design and implement an embedded SoPC (System on Programmable Chip) technique with Altera Nios II processor on a FPGA chip for real-time speech recognition system by developing hardware/software with minimum usage of resources (hardware components) and relatively small size software. This reduces the memory utilization, achieved by using Mel Frequency Cepstral Coefficients (MFCCs) technique as feature extraction combined with its first derivative ( MFCCs) including power computation of the speech frames (i.e. E,MFCC, E,and MFCC), called observation vector of the speech signal. To model the obtained observation, Gaussian Mixture Model (GMM) has been used, which is passed to a Hidden Markov Model (HMM) as probabilistic model to process the GMM statistically to take a decision on the uttered words recognition, whether a single or composite, one or more syllable words (i.e. one, six, welcome). The words that are used for training and testing the system included selected English and Arabic words.