A Hybrid Unsupervised Segmentation Algorithm for Arabic Speech Using Feature Fusion and a Genetic Algorithm (July 2018)

absa, Ahmed H. Abo; Deriche, Mohamed; Elshafei-Ahmed, M.; Elhadj, Yahya Mohamed; Juang, Biing‐Hwang

doi:10.1109/access.2018.2859631

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…Absa, et al [47] developed a hybrid speech segmentation algorithm using Genetic Algorithm (GA) optimization over multiple features, including entropy, zero crossing, and energy. Their results showed good accuracy compared to manual segmentation using the KACST database.…”

Section: Review Of Existing Arabic Segmentation Studiesmentioning

confidence: 99%

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Alqadasi,

Zeki,

Sunar

et al. 2024

IEEE Access

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Segmentation plays a crucial role in speech processing applications, where high accuracy is essential. The quest for improved accuracy in automatic segmentation, particularly in the context of the Arabic language, has garnered substantial attention. However, the differences between Qur'an recitation and normal Arabic speech, especially with regard to intonation rules affecting the lengthening of long vowels, pose challenges in segmentation especially for Qur'an recitation. This research endeavors to address these challenges by delving into the domain of automatic segmentation for Qur'an recitation recognition. The proposed scheme employs a hidden Markov models (HMMs) forced alignment algorithm. To enhance the precision of segmentation, several refinements have been introduced, with a primary emphasis on the phonetic model of the Qur'an and Tajweed, particularly the intricate rules governing elongation. These enhancements encompass the adaptation of an acoustic model tailored for Qur'anic recitation as preprocessing and culminate in the development of an algorithm aimed at refining forced alignment based on the phonetic nuances of the Qur'an. These enhancements are seamlessly integrated as post-processing components for the classic HMM-based forced alignment. The research utilizes a comprehensive database featuring recordings from 100 renowned Qur'an reciters, encompassing the recitation of 21 Qur'anic verses (Ayat). Additionally, 30 reciters were asked to record the same verses, incorporating various recitation speed patterns. To facilitate the evaluation process, a Random sample of the Qur'anic database was manually segmented, comprised 21 Ayats, totaling 19,800 words, with 89 unique words (14 verses x 3 recitation levels: fast, slow and normal x 6 readers). The outcomes of this study manifest notable advancements in the alignment of long vowels within Qur'an recitation, all while maintaining the precise alignment of vowels and consonants. Objective comparisons between the proposed automatic methods and manual segmentation were conducted to ascertain the superior approach. The findings affirm that the classic forced alignment method produces satisfactory outcomes when employed on verses lacking long vowels. However, its performance diminishes when confronted with verses containing long vowels. Therefore, the test samples were categorized into three groups based on the presence of long vowels, resulting in a Correct Classification Rate (CCR) that ranged from 6% to 57%, contingent on whether the verse includes long vowels or not. The average CCR across all test samples was 23%. In contrast, the proposed algorithm significantly enhances audio segmentation. It achieved CCR values ranging from 16% to 70% within the same database categories, with an average CCR of 45% across all test samples. This marks a notable advancement of 22% in segmented speech accuracy, particularly within a 30 ms tolerance, for verses containing long vowels.

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Section: Review Of Existing Arabic Segmentation Studiesmentioning

confidence: 99%

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Alqadasi,

Zeki,

Sunar

et al. 2024

IEEE Access

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show abstract

“…In [8], the authors produce a new speech segmentation algorithm for the Arabic language. Developing robust algorithms to accurately segment speech signals into fundamental units, rather than just frames, is a crucial preprocessing step in speech recognition systems.…”

Section: Other Hqsr Studiesmentioning

confidence: 99%

“…The solution of [5] uses Android speech recognition and depends heavily on third-party online services. In [8], they developed a robust hybrid speech segmentation system based on multiple features (entropy, zero crossings, and energy) and a GA-based optimization scheme to obtain accurate segment units specially adapted for Quran recitation.…”

Section: Hqsr Taxonomymentioning

confidence: 99%

Speech Recognition Models for Holy Quran Recitation Based on Modern Approaches and Tajweed Rules: A Comprehensive Overview

Al-Fadhli,

Al-Harbi,

Cherif

2023

IJACSA

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Speech is considered the most natural way to communicate with people. The purpose of speech recognition technology is to allow machines to recognize and understand human speech, enabling them to take action based on the spoken words. Speech recognition is especially useful in educational fields, as it can provide powerful automatic correction for language learning purposes. In the context of learning the Quran, it is essential for every Muslim to recite it correctly. Traditionally, this involves an expert gari who listens to the student's recitation, identifies any mistakes, and provides appropriate corrections. While effective, this method is time-consuming. To address this challenge, apps that help students fix their recitation of the Holy Quran are becoming increasingly popular. However, these apps require a robust and error-free speech recognition model. While recent advancements in speech recognition have produced highly accurate results for written and spoken Arabic and non-Arabic speech recognition, the field of Holy Quran speech recognition is still in its early stages. Therefore, this paper aims to provide a comprehensive literature review of the existing research in the field of Holy Quran speech recognition. Its goal is to identify the limitations of current works, determine future research directions, and highlight important research in the fields of spoken and written languages.

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“…For speech separation, different methods have been designed [9][10][11][12][13][14]. Approaches such as Computational Auditory Scene Analysis (CASA) [15][16][17][18], Hidden Markov Model (HMM) [19][20][21], HMM in conjunction with Cepstral Coefficients for Mel Frequency [22][23][24], Nonnegative Factorization of Matrix(NMF) [25][26][27][28] and Minimal Mean Square Error(MMSE) [29][30][31][32].…”

Section: Imentioning

confidence: 99%

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

Gupta

Singh²,

Sinha³

2020

Preprint

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Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.

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A Hybrid Unsupervised Segmentation Algorithm for Arabic Speech Using Feature Fusion and a Genetic Algorithm (July 2018)

Cited by 9 publications

References 34 publications

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Speech Recognition Models for Holy Quran Recitation Based on Modern Approaches and Tajweed Rules: A Comprehensive Overview

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

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