Makhraj Recognition of Hijaiyah Letter for Children Based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) Method

Makhraj is the most important thing for Muslim to recite the Holy Quran properly besides of Tajweed. This paper describe the Makhraj recognition of Hijaiyah Letter for children education. To make the Makhraj recognition, the feature extraction is used Mel-Frequency Cepstrum Coefficients (MFCC) method and to classify the Hijaiyah letter use Support Vector Machines (SVM) method based on Python 2.7. The waveform analysis of each Hijaiyah Makhraj pronunciation shows the differences of each letter. The database of Hijaiyah Makhraj pronunciation using 12 feature extraction can be classified by SVM process

Recognition of Correct Pronunciation for Arabic Letters Using Artificial Neural Networks

Automatic speech recognition (ASR) plays an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry and speech-to-text processing. The aim of this paper was to develop a voice-learning model for correct Arabic letter pronunciation using machine learning algorithms. The system was designed and implemented through three different phases: signal preprocessing, feature extraction and feature classification. MATLAB platform was used for feature extraction of voice using Mel Frequency Cepstrum Coefficients (MFCC). Matrix of MFCC features was applied to back propagation neural networks for Arabic letter features classification. The overall accuracy obtained from this classification was 65% with an error of 35% for one consonant letter, 87% accuracy and an error of 13% for 10 isolated different letters and 6 vowels each and finally 95% accuracy and an error of 5% for 66 different examples of one letter (vowels, words and sentences) stored in one voice file

Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling

In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible