Arabic speech segmentation: Automatic verses manual method and zero crossing measurements

Al-Manie, Mohammed A.

doi:10.17485/ijst/2010/v3i12.4

Cited by 7 publications

(2 citation statements)

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“…We performed manual segmentation of vowels in order to analyze the formant transition. Manual speech segmentation is based on listening and visual judgment in order to detect the required boundaries (Al-Manie, Alkanhal and Al-Ghamdi, 2010). The vowel boundaries refer to vowel onset and offset points.…”

Section: Discussionmentioning

confidence: 99%

Formant transitions as acoustic cues for place of articulation in speech perception

Jachova¹,

Ristovska²,

Spasov³

2021

godzbo

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Formant transitions reflect the overall change in the shape of the vocal tract during speech production. The objective of the study was to analyze F1 and F2 transition in Macedonian vowels in consonant context. This study included 12 native speakers of Macedonian (mean age of 42.7±11.9 years).Vowel /o/ in dento-alveolar plosive context /do/ had falling F2 transition in the duration of 38 ms and transition extent 772 Hz, and in velar plosive context /ɡo/ had falling F2 transition in the duration of 25 ms and transition extent 85 Hz. Vowel /a/ in alveolar fricative context /ʃa/ had falling F2 transition in the duration of 25 ms and transition extent 324 Hz, and in labial fricative context /fa/ had rising F2 transition in the duration of 38 ms and transition extent 214 Hz. Vowel /u/ in palatal fricative context /ju/ had falling F2 transition in the duration of 48 ms and transition extent 1084 Hz. F2 locus for dento-alveolar consonant was 1900 Hz, for velar 2100 Hz, for alveolar 1700 Hz, for labial 1200 Hz, and for palatal consonant was 2600 Hz.F2 locus is the highest for palatal consonants and the lowest for labial consonants. F2 transition is an acoustic cue for the place of articulation.

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Section: Discussionmentioning

confidence: 99%

Formant transitions as acoustic cues for place of articulation in speech perception

Jachova¹,

Ristovska²,

Spasov³

2021

godzbo

View full text Add to dashboard Cite

show abstract

“…Al-Manie, et al [44] employed the zero crossing rates (ZCR) algorithm to segment Arabic speech into various categories such as voiced/unvoiced fricatives, stops, nasals, laterals, and trills. Their study, conducted on the KACST dataset, yielded favorable results compared to manual segmentation.…”

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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Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition

Nahar

Shquier

Al-Khatib

et al. 2016

Int J Speech Technol

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Arabic speech segmentation: Automatic verses manual method and zero crossing measurements

Cited by 7 publications

References 0 publications

Formant transitions as acoustic cues for place of articulation in speech perception

Formant transitions as acoustic cues for place of articulation in speech perception

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition

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