ASR Error Management Using RNN Based Syllable Prediction for Spoken Dialog Applications

Abstract

We proposed automatic speech recognition (ASR) error management method using recurrent neural network (RNN) based syllable prediction for spoken dialog applications. ASR errors are detected and corrected by syllable prediction. For accurate prediction of a next syllable, we used a current syllable, previous syllable context, and phonetic information of next syllable which is given by ASR error. The proposed method can correct ASR errors only with a text corpus which is used for training of the target application, and it means that the method is independent to the ASR engine. The method is general and can be applied to any speech based application such as spoken dialog systems.