Abstract
As a continuation of our efforts towards tackling the problem of spoken Dialect Identification (DID) for Arabic languages, we present the QCRI-MIT Advanced Dialect Identification System (QMDIS). QMDIS is an automatic spoken DID system for Dialectal Arabic (DA). In this paper, we report a comprehensive study of the three main components used in the spoken DID task: phonotactic, lexical and acoustic. We use Support Vector Machines (SVMs), Logistic Regression (LR) and Convolutional Neural Networks (CNNs) as backend classifiers throughout the study. We perform all our experiments on a publicly available dataset and present new state-of-The-Art results. QMDIS discriminates between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and Modern Standard Arabic (MSA).We report ∼ 73% accuracy for system combination. All the data and the code used in our experiments are publicly available for research.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2591-2595 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2017-August |
| DOIs | |
| State | Published - 2017 |
| Event | 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden Duration: Aug 20 2017 → Aug 24 2017 |
Keywords
- Acoustic
- Arabic
- Convolutional Neural Network
- Lexical
- Logistic Regression
- Phonotactic
- Spoken Dialect Identification
- Support Vector Machine
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modeling and Simulation