CNN models for Maghrebian accent recognition with SVM silence elimination

Convolutional neural network (CNN) models have become very powerful machine learning due to their high classification accuracy especially for applications dealing with images. This paper presents an automatic system for Maghrebian (Algerian, Tunisian and Moroccan) accent recognition using CNN 2D model. The CNN model is fed by images constructed from the Mel frequency cepstral coefficients (MFCC) over time. Accents are recognized independently to utterance and its start time position within the speech signal. The silence part that is presented in speech signals, causes confusion in accent recognition. An SVM classifier is trained to remove the silence basing on 12 MFCC. To show the silence effect, accent recognition is performed with and without silence elimination using 5 folds cross validation technique. Many CNN architectures were studied and evaluated using 150 audio files of 30 speakers from the three Maghrebian accents for 1, 1.5 and 2 s test hearing durations (HD). The classification accuracy (CA) of the accent recognition is improved by about 2% after silence elimination and achieves 99.33% with a hearing duration of 1.5 s using the smallest best CNN model.