Automatic Recognition of Arabic Poetry Meter from Speech Signal using Long Short-term Memory and Support Vector Machine

Al-Talabani, Abdulbasit K. (2020) Automatic Recognition of Arabic Poetry Meter from Speech Signal using Long Short-term Memory and Support Vector Machine. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 8 (1). pp. 50-54. ISSN 2410-9355

Text (Research Article) ARO.10631-Vol8.No1.2020.ISSUE14-PP50-54.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (795kB)

Abstract

The recognition of the poetry meter in spoken lines is a natural language processing application that aims to identify a stressed and unstressed syllabic pattern in a line of a poem. Stateof-the-art studies include few works on the automatic recognition of Arud meters, all of which are text-based models, and none is voice based. Poetry meter recognition is not easy for an ordinary reader, it is very difficult for the listener and it is usually performed manually by experts. This paper proposes a model to detect the poetry meter from a single spoken line (“Bayt”) of an Arabic poem. Data of 230 samples collected from 10 poems of Arabic poetry, including three meters read by two speakers, are used in this work. The work adopts the extraction of linear prediction cepstrum coefficient and Mel frequency cepstral coefficient (MFCC) features, as a time series input to the proposed long short-term memory (LSTM) classifier, in addition to a global feature set that is computed using some statistics of the features across all of the frames to feed the support vector machine (SVM) classifier. The results show that the SVM model achieves the highest accuracy in the speakerdependent approach. It improves results by 3%, as compared to the state-of-the-art studies, whereas for the speaker-independent approach, the MFCC feature using LSTM exceeds the other proposed models.

Item Type:	Article
Uncontrolled Keywords:	Speech processing, Long short-term memory, Support vector machine, Prosody, Cepstral features
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	ARO-The Scientific Journal of Koya University > VOL 8, NO 1 (2020)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	05 Mar 2022 21:42
Last Modified:	05 Mar 2022 21:42
URI:	http://eprints.koyauniversity.org/id/eprint/233

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