Recognition of Modern Arabic Poems

Almuhareb, Abdulrahman; Almutairi, Waleed A.; Al-Tuwaijri, Haya; Almubarak, Abdulelah; Khan, Marwa

doi:10.17706/jsw.10.4.454-464

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…Speech signal data in the form of Malay Poems or known as Pantun is used to test the Brandt's GLR. The Pantun represents read mode audio data where audio recording is done in controlled environments and the speaker controls his/her manner of speech to ensure clear pronunciation and fixed reading pace [19,20]. The Poems (Pantuns) are framed per sentence.…”

Section: Methodsmentioning

confidence: 99%

“…This algorithm was adopted and modified to better locate the beginning and ending of speech points for the isolated spoken Malay utterances and will be discussed in detail. This is a two-step search algorithm where the absolute energy (AE) for a coarse search is first used [19]. The speech signal was first divided into 50% overlapping frames of 10ms and then passed through a rectangular window [30,31].…”

Section: Short-term Energy Algorithmmentioning

confidence: 99%

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Hybrid methods of brandt’s generalised likelihood ratio and short-term energy for malay word speech segmentation

Seman

Norazam

2019

IJEECS

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<p>Speech segmentation is an important part for speech recognition, synthesizing and coding. Statistical based approach detects segmentation points via computing spectral distortion of the signal without prior knowledge of the acoustic information proved to be able to give good match, less omission but lot of insertion. In this study the segmentation is done both manually and automatically using Malay words in traditional Malay poetry. This study proposed a hybrid method of Brandt’s generalized likelihood ratio (GLR) and short-term energy algorithm. The Brandt’s algorithm tries to estimate the abrupt change in energy to determine the segmentation points. A total of five Pantun are used in read mode and spoken by one male student in a noise free room. Experiments are conducted to see the the accuracy, insertion, and omission of the segmentation points. Experimental results show on average 80% accuracy with 0.2 second time tolerance for automatic segmentation with the algorithm having no knowledge of the acoustic characteristics<em>. </em></p>

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Section: Methodsmentioning

confidence: 99%

Section: Short-term Energy Algorithmmentioning

confidence: 99%

Hybrid methods of brandt’s generalised likelihood ratio and short-term energy for malay word speech segmentation

Seman

Norazam

2019

IJEECS

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“…This algorithm was implemented using Turbo C under MS-DOS and this tool cannot be used in present operating systems (Shalabi et al, 2003). Almuhareb et al (2013) used a method for extracting and recognizing the old Arabic poetry found in poetic texts.…”

Section: Related Workmentioning

confidence: 99%

Arabic Poetry Authorship Attribution using Machine Learning Techniques

Ahmed¹,

Ramdani²,

Bellafkih³

2019

Journal of Computer Science

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In this study, authorship attribution in Arabic poetry will be conducted to determine the authorship of a specified text after documents with recognized authorships have been allocated. This work also measures the impact performance of Naïve Bayes, Support Vector Machine and Linear discriminant analysis for Arabic poetry authorship attribution using text mining classification. Several features such as lexical features, character features, structural features, poetry features, syntactic features, semantic features and specific word features are utilized as the input data for text mining, using classification algorithms Linear discriminant analysis, Support Vector Machine and Naïve Bayes by Arabic Poetry Authorship Attribution Model (APAAM). The dataset of Arabic poetry is divided into two sets: known poetic in training dataset texts and anonymous poetic texts in a test dataset part. In the experiment, a set of 114 random poets from entirely different eras are used. The highest performance accuracy value is 99, 12%; the performance rate at the attribute level is 98.246%; the level of techniques is 92.836%.

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“…Each vowel sound has two versions: a long and a short version. Short vowels are written as diacritical marks below or above the letter that precedes them while long vowels are written as whole letters [16].…”

Section: B Rhyme (Qafiya)mentioning

confidence: 99%