Abstract
In this paper, we propose to compare four methods for speech understanding of under-resourced languages. The first method is knowledge based method which integrates ontology. The second one is statistical method which integrates CRF discriminative models. The third one is hybrid method which uses CRF models with integration of knowledge base. The fourth one is pattern based method. We have used a spoken Tunisian dialect corpus acquired and annotated to perform experiments. The evaluation is based on semantic representation generated by each method. The obtained results shows that the hybrid method is the best one compared to others, which proves that CRF models with ontology integration is suitable for under-resourced languages.