Deep Study of CRF Models for Speech understanding in Limited Task

In this paper, we propose to evaluate in depth CRF models (Conditional Random Fields) for speech-understanding in limited task. To evaluate these models, we design several models that differ according to the level of integration of local dependencies in the same turn. As we propose to evaluate these models on different types of processed data. We perform our study on a corpus where turns are not segmented into utterances. In fact, we propose to use the whole turn as one unit during training and testing of CRF models. This represents the natural way of conversation. The language used in this work is the Tunisian Arabic dialect. The obtained results prove the robustness of CRF models when dealing with raw data. They are able to detect the semantic dependency between words in the same speech turn. Results are important when CRF models are designed to take into account the words with deep dependencies in the same turn and with advanced preprocessed data.