The contribution of prosody to the identification of Persian regional accents

This paper focuses on the contribution of prosody to the identification of some Persian regional accents. In this way, we measured prosodic features including rhythm-related features and global statistics on pitch contour, energy contour and their derivatives. Then sequential forward feature selection is employed to identify the most discriminant attributes to classify speakers according to their accents. Major identified accent-specific features are related to the derivative of pitch contour. For this purpose, we have recorded a corpus containing speech from different Persian accents, including Turkish, Kordish, Tehrani, Isfahani and Mazandarani. Also to understand human ability to detect accents, a perceptual experiment has been arranged. The automatic accent classification rate using the identified best features and employing a SVM classifier is obtained and compared to human performance.