automatic language identification for berber and arabic languages using prosodic features
Khlaed Lounnas, Lyes Demri, Leila Falek, Hocine Teffahi · 2018
This paper describes the design of an automatic identification system for the distinction between two common languages in Algeria which are MSA (Modern Standard Arabic) and Kabyle which is an Algerian Berber dialect. The characteristics used for this are prosodic (melody and stress) and cepstral (Mel Frequency Cepstral Coefficients) features extracted from a bilingual database formed by combining two databases. For the classification step Support vector machines are trained for language modeling because it is a recognition of two languages which puts him in a better position to establish this task. Experiments have shown the superior reliability of our system when using both types of characteristics when compared to the use of each type separately. It was found that when using prosodic characteristics the system gives an encouraging rate of 95.42% which was improved to 95.83% when adding acoustic features and 97.5% (so an improvement of 1.67%) when combining prosodic and acoustic features which has shown the superiority of this vector over those previously cited.