ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications

Abstract

We proposed an automatic speech recognition (ASR) error correction method using hybrid word sequence matching and recurrent neural network for dialog system applications. Basically, the ASR errors are corrected by the word sequence matching whereas the remaining OOV (out of vocabulary) errors are corrected by the secondary method which uses a recurrent neural network based syllable prediction. We evaluated our method on a test parallel corpus (Korean) including ASR results and their correct transcriptions. Overall result indicates that the method effectively decreases the word error rate of the ASR results. The proposed method can correct ASR errors only with a text corpus without their speech recognition results, which 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.