RNN Language Processing Model-Driven Spoken Dialogue System Modeling Method

Abstract

Speech recognition and semantic understanding of spoken language are critical components in determining the dialogue system’s performance in SDS. In the study of SDS, the improvement of SLU performance is critical. By influencing the factors before and after the input text sequence information, RNN predicts the next text information. The RNN language model’s probability score is introduced, and the recognition’s intermediate result is rescored. A method of combining cache RNN models to optimize the decoding process and improve the accuracy of word sequence probability calculation of language model on test data is proposed to address the problem of mismatch between test data and training data in recognition. The results of the experiments show that the method proposed in this paper can effectively improve the recognition system’s performance on the test set. It has the potential to achieve a higher SLU score. It is useful for future research on spoken dialogue and SLU issues.