Linguistic processing using a dependency structure grammar for speech recognition and understanding

This paper proposes an efficient linguistic processing strategy for speech recognition and understanding using a dependency structure grammar. The strategy includes parsing and phrase prediction algorithms. After speech processing and phrase recognition based on phoneme recognition, the parser extracts the sentence with the best likelihood taking account of the phonetic likelihood of phrase candidates and the linguistic likelihood of the semantic inter-phrase dependency relationships. A fast parsing algorithm using breadth-first search is also proposed. The predictor pre-selects the p}~.ase candidates using transition rules combined with a dependency structure to reduce the amount of phonetic processing. The proposed linguistic processor has been tested through speech recognition experiments. The experimental results show that it greatly increases the accuracy of speech recognitions, and the breadth-first parsing algorithm and predictor increase processing speed.