Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts
Areej Alshutayri, Eric Atwell, Abdulrahman Alosaimy, James Dickins, Michael Ingleby, Janet Watson
Abstract
This paper describes an Arabic dialect identification system which we developed for the Discriminating Similar Languages (DSL) 2016 shared task. We classified Arabic dialects by using Waikato Environment for Knowledge Analysis (WEKA) data analytic tool which contains many alternative filters and classifiers for machine learning. We experimented with several classifiers and the best accuracy was achieved using the Sequential Minimal Optimization (SMO) algorithm for training and testing process set to three different feature-sets for each testing process. Our approach achieved an accuracy equal to 42.85% which is considerably worse in comparison to the evaluation scores on the training set of 80-90% and with training set “60:40” percentage split which achieved accuracy around 50%. We observed that Buckwalter transcripts from the Saarland Automatic Speech Recognition (ASR) system are given without short vowels, though the Buckwalter system has notation for these. We elaborate such observations, describe our methods and analyse the training dataset.- Anthology ID:
- W16-4826
- Volume:
- Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Preslav Nakov, Marcos Zampieri, Liling Tan, Nikola Ljubešić, Jörg Tiedemann, Shervin Malmasi
- Venue:
- VarDial
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 204–211
- Language:
- URL:
- https://aclanthology.org/W16-4826
- DOI:
- Cite (ACL):
- Areej Alshutayri, Eric Atwell, Abdulrahman Alosaimy, James Dickins, Michael Ingleby, and Janet Watson. 2016. Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 204–211, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts (Alshutayri et al., VarDial 2016)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/W16-4826.pdf