Spoken Arabic Algerian dialect identification

Soumia Bougrine, Hadda Cherroun, Ahmed Abdelali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Dialect identification is a challenging task and this becomes more complicated when dealing with under-resourced dialects. In this paper, we propose a system based on prosodic speech information, namely intonation and rhythm for identification of Intra-country dialects. The speech features are extracted after a coarse-grained consonant/vowel segmentation. Dialect models are built using both Deep Neural Networks (DNNs) and SVM. The hyper-parameters for the DNNs topology are tuned using a genetic algorithm. Our framework is implemented and evaluated on KALAM'DZ, a Web-based corpus dedicated to Algerian Arabic Dialectal varieties, with more than 42 h encompassing the four major Algerian subdialects: Hilali, Su-laymite, Ma'qilian, and Algiers-blanks. The results show that the DNNs implementation of Algerian Arabic Dialect IDentification system (a2did) reaches the same results when compared to SVM modeling. In addition, we concluded that a contrastive baseline acoustic-based classification system can serve as a complementary system to our a2did. The overall results reveal the suitability of our prosody-based a2did for speaker-independent dialect identification when utterances size are short. A requirement for real-time applications.

Original languageEnglish
Title of host publication2nd International Conference on Natural Language and Speech Processing, ICNLSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538645437
DOIs
Publication statusPublished - 6 Jun 2018
Event2nd International Conference on Natural Language and Speech Processing, ICNLSP 2018 - Algiers, Algeria
Duration: 25 Apr 201826 Apr 2018

Other

Other2nd International Conference on Natural Language and Speech Processing, ICNLSP 2018
CountryAlgeria
CityAlgiers
Period25/4/1826/4/18

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Keywords

  • Algerian dialects
  • Deep Neural Networks
  • Dialect Identification
  • Prosody

ASJC Scopus subject areas

  • Linguistics and Language
  • Communication
  • Artificial Intelligence
  • Signal Processing

Cite this

Bougrine, S., Cherroun, H., & Abdelali, A. (2018). Spoken Arabic Algerian dialect identification. In 2nd International Conference on Natural Language and Speech Processing, ICNLSP 2018 (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNLSP.2018.8374383