Development of Quranic Reciter Identification System using MFCC and GMM Classifier

Gunawan, Teddy Surya; Saleh, Nur Atikah Muhamat; Kartiwi, Mira

doi:10.11591/ijece.v8i1.pp372-378

Cited by 10 publications

(13 citation statements)

References 11 publications

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“…In Gunawan et al [23], for Qur'anic reciter identification, the features of Mel Frequency Cepstral Coefficients (MFCC) were extracted from the recorded audio, and after training a Gaussian Mixture Model (GMM), Gaussian Supervectors (GSVs) were formed using model parameters such as the mean vector and the main diagonal of the covariance matrix. This model can be applied to protocol classification, feature learning, anomalous protocol identification, and unknown protocol classification.…”

Section: Nahar Et Al Inmentioning

confidence: 99%

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

Hadwan¹,

Alsayadi²,

Al-Hagree³

2023

Computers, Materials &Amp; Continua

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The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-toend transformer-based models for the Arabic language, as the researchers' community pays little attention to it. The Muslims Holy Qur'an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur'an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic model. A Mel filter bank is used for feature extraction. To build a language model (LM), the Recurrent Neural Network (RNN) and Long short-term memory (LSTM) were used to train an n-gram word-based LM. As a part of this research, a new dataset of Qur'an verses and their associated transcripts were collected and processed for training and evaluating the proposed model, consisting of 10 h of .wav recitations performed by 60 reciters. The experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate (CER) of 1.98% and a word error rate (WER) of 6.16%. We have achieved state-of-the-art end-to-end transformer-based recognition for Qur'an reciters.

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Section: Nahar Et Al Inmentioning

confidence: 99%

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

Hadwan¹,

Alsayadi²,

Al-Hagree³

2023

Computers, Materials &Amp; Continua

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“…The Mel-frequency cepstral coefficients (MFCC) is one of the most popular audio feature [12,13]. It is a representation of the speech signals where a feature called the cepstrum of a windowed short-time signal is derived from the FFT of that signal.…”

Section: Mel-frequency Cepstral Coefficients (Mfcc)mentioning

confidence: 99%

A Review on Emotion Recognition Algorithms using Speech Analysis

Gunawan

Alghifari

Morshidi

et al. 2018

IJEEI

Self Cite

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In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Melfrequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture model (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed.

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“…In most past studies, researchers used a combination of the aforementioned features to obtain the best accuracy of emotion detection [7,11,19]. Although various techniques have been investigated in feature extraction and classification stage in speech processing [20], there is scarce resources on pre-processing stage in ERS. Since frame blocking is necessary in speech processing, selecting appropriate frames before feature extraction is important task to improve the performance of ERS because not all frames contain emotion attribute especially when global features of an utterance is taken such in this paper.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-based voiced-unvoiced segmentation for emotion recognition using spectral feature fusions

Ali

Rahim

Noorsal

et al. 2020

IJEECS

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Despite abundant growth in automatic emotion recognition system (ERS) studies using various techniques in feature extractions and classifiers, scarce sources found to improve the system via pre-processing techniques. This paper proposed a smart pre-processing stage using fuzzy logic inference system (FIS) based on Mamdani engine and simple time-based features i.e. zero-crossing rate (ZCR) and short-time energy (STE) to initially identify a frame as voiced (V) or unvoiced (UV). Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) were tested with K-nearest neighbours (KNN) classifiers to evaluate the proposed FIS V-UV segmentation. We also introduced two feature fusions of MFCC and LPC with formants to obtain better performance. Experimental results of the proposed system surpassed the conventional ERS which yielded a rise in accuracy rate from 3.7% to 9.0%. The fusion of LPC and formants named as SFF LPC-fmnt indicated a promising result between 1.3% and 5.1% higher accuracy rate than its baseline features in classifying between neutral, angry, happy and sad emotions. The best accuracy rates yielded for male and female speakers were 79.1% and 79.9% respectively using SFF MFCC-fmnt fusion technique.

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Development of Quranic Reciter Identification System using MFCC and GMM Classifier

Cited by 10 publications

References 11 publications

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

A Review on Emotion Recognition Algorithms using Speech Analysis

Fuzzy-based voiced-unvoiced segmentation for emotion recognition using spectral feature fusions

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