NDLI: Spoken Arabic Digits Recognition Based on (GMM) for E-Quran Voice Browsing: Application for Blind Category

Content Provider	IEEE Xplore Digital Library
Author	Hammami, N. Bedda, M. Farah, N. Lakehal-Ayat, R.O.
Copyright Year	2013
Description	Author affiliation: Lab. LabGed, Univ. Badji Mokhtar Annaba, Annaba, Algeria (Hammami, N.; Farah, N.) \|\| Algiers III Univ., Algiers, Algeria (Lakehal-Ayat, R.O.) \|\| Fac. of Eng., Al Jouf Univ., Sakaka, Saudi Arabia (Bedda, M.)
Abstract	People with low or no visual ability must also be able to manipulate, operate and browse the electronic reading devices of the Quran by a simple use of the voice (operation known as Voice-In/Voice-Out). The main operations of navigation and exploration of these devices, as the movement between verses or between pages can be fully realized through a voice recognition system of Arabic numbers. In this paper, we propose the use of voice recognition of Arabic digits as a way to use these devices, for this purpose, we present the method of speech recognition based on: (GMM) classifier, known for its effectiveness and scalability in speech modeling and the leading approach in speech recognition feature extraction Delta-Delta Mel-frequency cepstral coefficients (MFCC). The experimental results with the obtained parameters demonstrate the effectiveness of the digit recognition on a dataset in 99.31% of cases, which is highly satisfactory compared to previous works on spoken Arabic digits speech recognition.
Starting Page	123
Ending Page	127
File Size	378258
Page Count	5
File Format	PDF
e-ISBN	9781479928231
DOI	10.1109/NOORIC.2013.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-22
Publisher Place	Saudi Arabia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Conferences Mel frequency cepstral coefficient Voice-In/Voice-Out Arabic spoken digits DDMFCC E-Quran voice browsing Hidden Markov models Arabic speech recognition Speech recognition Speech Feature extraction Gaussian mixture model (GMM) Speech processing MFCC
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Spoken Arabic Digits recognition using MFCC based on GMM

The second-order derivatives of MFCC for improving spoken Arabic digits recognition using Tree distributions approximation model and HMMs

A novel approach for MFCC feature extraction

Voice Content Matching System for Quran Readers

Emotion recognition in speech using MFCC and wavelet features

Hybrid of wavelet and MFCC features for speaker verification

Speech recognizer for regional languages of Pakistan

Spoken arabic digits recognition based on wavelet neural networks

Comparison of voice features for Arabic speech recognition

Spoken Arabic Digits Recognition Based on (GMM) for E-Quran Voice Browsing: Application for Blind Category

Similar Documents

Spoken Arabic Digits recognition using MFCC based on GMM

The second-order derivatives of MFCC for improving spoken Arabic digits recognition using Tree distributions approximation model and HMMs

A novel approach for MFCC feature extraction

Voice Content Matching System for Quran Readers

Emotion recognition in speech using MFCC and wavelet features

Hybrid of wavelet and MFCC features for speaker verification

Speech recognizer for regional languages of Pakistan

Spoken arabic digits recognition based on wavelet neural networks

Comparison of voice features for Arabic speech recognition

Spoken Arabic Digits Recognition Based on (GMM) for E-Quran Voice Browsing: Application for Blind Category