Spoken Arabic Algerian dialect identification
Hadda Cherroun Soumia Bougrine, Ahmed Abdelalí · 2018
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.