A unified framework to incorporate speech and language information in spoken language processing

To enhance the performance of spoken language processing, a unified framework is proposed to integrate speech and language information together. This framework uses probabilistic formulation to characterize different language analyses generated from a language processing module. As probabilistic formulations are used in both speech and language processing modules, information from both modules can be easily integrated. To further improve the performance, a discrimination and robustness oriented learning procedure is proposed to adjust the parameters of probabilistic formulations. Significant improvement has been observed in the task of reading aloud Chinese computer manuals, which operates in a speaker dependent, isolated word mode