Automatic segmentation of Arabic speech signals by HMM and ANN

Abed, Abir El; Amrouche, Abderrahmane; Abdelkader, Delmadji; Khadidja, Boubakeur; Droua-Hamdani, Ghania

doi:10.1109/cistem.2016.8066776

Cited by 14 publications

(3 citation statements)

References 7 publications

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“…Ahcène, et al [50] introduced an automatic segmentation system for Arabic speech into phonemes using a combination of HMM and ANN, classifying speech signals into five classes: fricatives, plosives, nasals, liquids, and vowels.…”

Section: Review Of Existing Arabic Segmentation Studiesmentioning

confidence: 99%

“…The efficacy of forced alignment is gauged through the computation of the Correct Classification Rate (CCR), denoted as: This metric quantifies the percentage of frames that are correctly categorized. In this context, a frame is deemed accurately classified when it aligns with the same class as in manual segmentation [50].…”

Section: Figure 8 Sample Of the Forced Alignment Output File (A) Phon...mentioning

confidence: 99%

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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%

Section: Figure 8 Sample Of the Forced Alignment Output File (A) Phon...mentioning

confidence: 99%

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Alqadasi,

Zeki,

Sunar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…HMM models the class information well, but it may not detect the exact boundary. Another method based on HMM and designed for Arabic language is (Abed et al, 2016) that proposes an automatic segmentation system of speech into phonemes for the Arabic language. This segmentation is based on two different techniques: Hidden Markov Models (HMM) and Artificial Neural Networks (ANN).…”

Section: Word (N)mentioning

confidence: 99%

Speech Segmentation Using Dynamic Windows and Thresholds for Arabic and English Languages

Jazyah¹

2018

Journal of Computer Science

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Segmentation of audio data such as human speech (splitting each word in separate audio file-.WAV file) has been a major concern when working with multimedia such as recordings from radio or TV. The main focus of the segmentation of boundaries of spoken language has been on using energy and zero crossing thresholds for endpoint detection. Errors in endpoint detection are still a main cause of low accuracy of segmentation systems. The goal of this research is to develop an efficient algorithm in order to segment the speech of human in both languages of English and Arabic in different speaking speed with high accuracy. Simulation results show that the developed algorithm achieved high accuracy when segmenting human speech in English language up to 91.6% in average, while it is 89.0% of Arabic language.

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Analysis of the Influence of the Arabic Fricatives Vocalic Context on Their Spectral Parameters

Zitouni

Falek

Abbas³

et al. 2021

Advances in Computing Systems and Applications

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Automatic segmentation of Arabic speech signals by HMM and ANN

Cited by 14 publications

References 7 publications

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Speech Segmentation Using Dynamic Windows and Thresholds for Arabic and English Languages

Analysis of the Influence of the Arabic Fricatives Vocalic Context on Their Spectral Parameters

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