FPGA Implementation of Speech Recognition System Based on HMM - Couverture souple

Refeis, Alaa; Abbas, Eyad

 
9783847346029: FPGA Implementation of Speech Recognition System Based on HMM
Couverture souple
ISBN 10 : 3847346024 ISBN 13 : 9783847346029
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

B.Sc. Electronics and Communication Engineering.Higher Diploma Computer Science / Artificial Intelligence.M.Sc. Electronics Engineering.GSM mobile communication.Computer Networking/Administration.Digital System Design.Embedded Systems(SoPC).

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2014
Langue
anglais
ISBN 10 
3847346024
ISBN 13 
9783847346029
Reliure
Broché
Nombre de pages
132
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne