Optimizing Parameters of Automatic Speech Segmentation into Syllable Units

Karim, Riksa Meidy; Suyanto, Suyanto

doi:10.5815/ijisa.2019.05.02

Cited by 19 publications

(3 citation statements)

References 14 publications

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“…Karim [48] proposed three independent Genetic Algorithms (GAs) for optimizing the parameters of Indonesian syllable speech segmentation, focusing on boundary detection, iterative splitting, and iterative assimilation.…”

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

“…[ Karim and Suyanto 2019] propõem um modelo de segmentação silábica automática baseado em características da energia ao longo do tempo que utiliza um limiar otimizado por meio de algoritmos genéticos (GA) para encontrar as fronteiras de cada sílaba. Para isso, ele utiliza uma abordagem iterativa para dividir as palavras e para aglutinar sílabas que tenham um núcleo silábico comum.…”

Section: Trabalhos Relacionadosunclassified

Identificação de silabação em áudios de leitura de crianças em anos iniciais

Jucá,

Rocha,

Mello

et al. 2023

Anais Do XXXIV Simpósio Brasileiro De Informática Na Educação (SBIE 2023)

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Este artigo aborda a detecção automática de silabação em áudios de crianças em fase de alfabetização, que é um desafio da avaliação de fluência em leitura. Nesse contexto, o reconhecimento automático da fala (ASR) permite processar os áudios de forma rápida e objetiva, gerando métricas acústicas sobre a duração das sílabas e a duração dos intervalos entre elas. Assim, propõe-se neste trabalho a aplicação de heurísticas que usam essas características para classificar automaticamente a silabação. Os resultados obtidos alcançaram acurácia de 0,87 em uma base de validação, o que destaca que a classificação automática da silabação pode ser aplicada na avaliação de fluência.

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“…Therefore, many probabilistic algorithms are developed to tackle optimization problems, such as genetic algorithm (GA) [10], [11], [12], [13], [14], particle swarm optimization (PSO) [15], [16], bee colony optimization [17], [18], cuckoo search [19], and Firefly Algorithm (FA) [20], [21], [22], [23]. There are also many new their hybrid versions or variants, such as parallel genetic algorithm [24], fuzzy optimization [25], ant colony optimization and variable neighbourhood search (ACO-VNS) [26], hybrid firefly algorithm (HFA) [27], and Hybrid Evolutionary Firefly Algorithm (HEFA) [28].…”

Section: Introductionmentioning

confidence: 99%

Clustering Nodes and Discretizing Movement to Increase the Effectiveness of HEFA for a CVRP

Abdillah¹,

Suyanto²

2020

IJACSA

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A Capacitated Vehicle Routing Problem (CVRP) is an important problem in transportation and industry. It is challenging to be solved using some optimization algorithms. Unfortunately, it is not easy to achieve a global optimum solution. Hence, many researchers use a combination of two or more optimization algorithms, which based on swarm intelligence methods, to overcome the drawbacks of the single algorithm. In this research, a CVRP optimization model, which contains two main processes of clustering and optimization, based on a discrete hybrid evolutionary firefly algorithm (DHEFA), is proposed. Some evaluations on three CVRP cases show that DHEFA produces an averaged effectiveness of 91.74%, which is much more effective than the original FA that gives mean effectiveness of 87.95%. This result shows that clustering nodes into several clusters effectively reduces the problem space, and the DHEFA quickly searches the optimum solution in those partial spaces.

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Optimizing Parameters of Automatic Speech Segmentation into Syllable Units

Cited by 19 publications

References 14 publications

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Identificação de silabação em áudios de leitura de crianças em anos iniciais

Clustering Nodes and Discretizing Movement to Increase the Effectiveness of HEFA for a CVRP

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