Detection of Questions in Arabic Audio Monologues Using Prosodic Features

Abstract

Prosody has been widely used in many speech-related applications including speaker and word recognition, emotion and accent identification, topic and sentence segmentation, and text-to-speech applications. An important application we investigate is that of identifying question sentences in Arabic monologue lectures. Languages other than Arabic have received a lot of attention in this regard. We approach this problem by first segmenting the sentences from the continuous speech using intensity and duration features. Prosodic features are, then, extracted from each sentence. These features are used as input to decision trees to classify each sentence into either question or non question sentence. Our results suggest that questions are cued by more than one type of prosodic features in natural Arabic speech. We used C4.5 decision trees for classification and achieved 75.7% accuracy. Feature specific analysis further reveals that energy and fundamental frequency features are mainly responsible for discriminating between questions and non-question sentences.