Classification of regional accent using speech rhythm metrics

Abstract

In this paper, MSA speech rhythm metrics were used to classify two regional accent (northern vs. southern regions) using an MLP - neural network classifier. Seven rhythm metrics vectors were computed from a speech dataset taken from ALGerian Arabic Speech Database (ALGASD) using both Interval Measures (IM) and Control/Compensation Index (CCI) algorithms. The classifier was trained and tested using different input vectors of speech rhythm measurements. The best accuracy of the NN-classifier was achieved when a combination of all metrics was used (88.6%).