Natural Tunisian Speech Preprocessing for Features Extraction.

In this paper, we describe the process of building a corpus for Tunisian Speech Emotion Recognition (SER). To the best of our knowledge, it is the first work in the SER field that uses spontaneous speech emotion in Tunisian dialect. SER represents an active research problem in the field of Natural Language processing (NLP). It aims to detect different emotions such as satisfaction, frustration and anger from audio speeches using various classifiers. Speech signal preprocessing is the first and the most important step in the SER process. Moreover, Pre-Processing of Speech is very crucial in the applications where silence or ambient noise is completely undesirable. Voice activity detection is a common procedure that plays a key role in preprocessing speech signals and noise cancellation. Pre-emphasis of speech helps the system be computationally more effective [1].This work proposed a preprocessing method to extract features from natural Tunisian speech. Speech preprocessing consists of cleaning the speech signal from ambient and unwanted noises, detecting speech activity and normalizing the length of the vocal tract.